Genes and gene signatures for diagnosis and treatement of melanoma

ABSTRACT

Panels of biomarkers, methods and systems are disclosed for determining gene expression, and diagnosing and treating melanoma.

RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 14/205,965, filed Mach 12, 2014, which claims thebenefit of U.S. Provisional Application No. 61/793,031, filed Mar. 15,2013, and U.S. Provisional Application No. 62/889,609, filed Oct. 11,2013, each of which are entirely incorporated herein by reference.

FIELD OF THE INVENTION

The invention generally relates to a molecular classification of diseaseand particularly to genes and gene signatures for diagnosis of melanomaand methods of use thereof.

TABLES

The instant application was filed with five (4) Tables under 37 C.F.R.§§ 1.52(e)(1)(iii) & 1.58(b), submitted electronically as the followingtext files:

Table WW:

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Table XX:

File name: “3330-01-1P-2013-03-15-TABLEXX-MSG.txt”

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Table YY:

File name: “3330-01-1P-2013-03-15-TABLEYY-MSG.txt”

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Table ZZ:

File name: “3330-01-1P-2013-03-15-TABLEZZ-MSG.txt”

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LENGTHY TABLES The patent contains a lengthy table section. A copy ofthe table is available in electronic form from the USPTO web site(https://seqdata.uspto.gov/?pageRequest=docDetail&DocID=US11834719B2).An electronic copy of the table will also be available from the USPTOupon request and payment of the fee set forth in 37 CFR 1.19(b)(3).

Each of the above files and all their contents are incorporated byreference herein in their entirety.

BACKGROUND OF THE INVENTION

In the United States, over 76,000 new cases of melanoma will bediagnosed in 2013. American Cancer Society, FACTS AND FIGURES 2013.Treatment of melanoma at an earlier stage is associated with highersurvival rates in patients. There is therefore a great need for advancesin methods of early diagnosis and treatment of melanoma.

BRIEF SUMMARY OF THE INVENTION

Panels of biomarkers, methods and systems are disclosed for determininggene expression, and diagnosing and treating melanoma.

In a first aspect the disclosure is related to methods of diagnosingmelanoma in a patient. In general, said methods comprise measuring in asample obtained from the patient the expression of one or more genes, ora panel of genes. The genes may be cell cycle genes, immune genes oradditional genes as defined herein. The genes may be selected from Table1, Table 3, or one of the many specifically defined panels (Panels A-I,or panels in Tables WW-ZZ). The method may also comprise comparing themeasured expression levels of the one or more genes to the expressionlevels of the same one or more genes measured in a reference sample.Detecting a difference in the expression levels of the one or more genesindicates that the patient has melanoma.

In a second aspect, the disclosure is related to methods of detectingabnormal levels of gene expression in a skin lesion. In general, themethods comprise measuring in a skin lesion obtained from a patient theexpression of one or more genes, or a panel of genes. The genes may becell cycle genes, immune genes or additional genes as defined herein.The genes may be selected from Table 1, Table 3, or one of the manyspecifically defined panels (Panels A-I, or panels in Tables WW-ZZ). Themethod may also comprise comparing the measured expression levels of theone or more genes to the expression levels of the same one or more genesmeasured in a reference sample. The method may also comprise detectingan abnormal level of gene expression of at least one of the one or moregenes.

In a third aspect, the disclosure is related to treating a patient withmelanoma. In general, the methods comprise measuring in a skin lesionobtained from a patient the expression of one or more genes, or a panelof genes. The genes may be cell cycle genes, immune genes or additionalgenes as defined herein. The genes may be selected from Table 1, Table3, or one of the many specifically defined panels (Panels A-I, or panelsin Tables WW-ZZ). The method may also comprise comparing the measuredexpression levels of the one or more genes to the expression levels ofthe same one or more genes measured in a reference sample. The methodmay also comprise detecting an abnormal level of gene expression of atleast one of the one or more genes, and altering the patient's treatmentbased at least in part on the difference.

Also disclosed are systems, compositions and kits to aid in detectingabnormal levels of gene expression, diagnosing melanoma or treatingmelanoma.

Unless otherwise defined, all technical and scientific terms used hereinhave the same meaning as commonly understood by one of ordinary skill inthe art to which this invention pertains. Although methods and materialssimilar or equivalent to those described herein can be used in thepractice or testing of the present invention, suitable methods andmaterials are described below. In case of conflict, the presentspecification, including definitions, will control. In addition, thematerials, methods, and examples are illustrative only and not intendedto be limiting.

Other features and advantages of the invention will be apparent from thefollowing detailed description, and from the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a system for performing computer-assisted methods ofdiagnosing, detecting, screening and/or treating melanoma in a patient.

FIG. 2 shows the distribution of the CCP scores from all 30 samples witha score and separated by clinical diagnosis. The melanoma samples havestatistically different distributions when compared to the nevi samples.

FIG. 3 shows the distributions of all 88 individual amplicon assaystested in Rounds 1, 2, and 3 of biomarker discovery. The analysis wasperformed on 30 Group 1 samples (black circles) and 53 Group 2 samples(grey circles).

FIG. 4 shows distributions of each individual amplicon tested in Round 1and 2 of biomarker discovery. Samples are differentiated based on theirpathological subtype on the Y-axis. The relative expression (Ct) of eachgene (compared to the expression of the housekeeper genes) is graphed onthe X-axis. Each amplicon is identified by the gene name and the lastthree digits of the assay ID.

FIG. 5 shows the normalized expression of PRAME in each sample, asdifferentiated by both site and histological subtype. Malignant samplesare black, while benign samples are colored grey.

FIG. 6 shows the expression of each of the best 8 immune genes. Each ofthe genes had a linear relationship with the average expression of all 8of the immune genes (indicating that they all measure the samebiological process). Furthermore, all immune genes can differentiatemelanoma and nevi samples (black and grey colored, respectively).

FIG. 7 shows graphs of the expression of each marker (of PRAME, theaverage of the 8 immune genes, and S100A9) graphed against the othermarkers. Data from each site graphed separately. The lack of highcorrelation between each biomarker indicates that they each are likelymeasuring different biological processes and each has independent value.

FIG. 8 shows the score generated by the diagnostic model, using theexpression of PRAME, the immune genes, and S100A9. This score was usedto differentiate malignant melanoma and benign nevi.

FIG. 9 shows an AUROC curve generated from the dataset, using the scoreproduced from the model. The AUC of the ROC curve is ˜0.96.

FIG. 10 shows the distribution of scores (x-axis) from all testedsamples. The data are differentiated by primary diagnosis. The top panelshows scores for malignant samples. The bottom panel shows scores ofbenign samples.

FIG. 11 shows an AUROC curve generated from the dataset based on theability of the model to differentiate melanoma and nevi samples. The AUCof the ROC is ˜0.95. Sensitivity and specificity are shown.

FIG. 12 shows the distribution of scores (x-axis) from all testedsamples for the validation cohort. The data are differentiated byprimary diagnosis. The top panel shows scores for malignant samples. Thebottom panel shows scores of benign samples.

FIG. 13 shows an AUROC curve generated based on the validation cohort.The AUC of the ROC is ˜0.96. Sensitivity and specificity are shown.

DETAILED DESCRIPTION OF THE INVENTION

Genes and Panels

Disclosed herein are gene biomarkers and panels of biomarkers, methodsand systems for determining gene expression, and methods for diagnosingand treating melanoma. It should be understood that the methods andsystems disclosed are all intended to be utilized in conjunction withbiomarkers as described herein. In particular, any panel disclosed maybe used with any method or system of this disclosure. Furthermore,subpanels of any panel disclosed may furthermore be used, as describedbelow.

The gene biomarkers and panels of biomarkers are useful, at least inpart, for their predictive power in determining whether an individualhas melanoma. It has been discovered that the predictive power of apanel or group of genes often ceases to increase significantly beyond acertain number. More specifically, the optimal number of genes in apanel, or used to generate a test value can be found wherever thefollowing is true

(Pn + 1 − Pn) < CO,wherein P is the predictive power (i.e., Pn is the predictive power of asignature with n genes and Pn+1 is the predictive power of a signaturewith n genes plus one) and CO is some optimization constant. Predictivepower can be defined in many ways known to those skilled in the artincluding, but not limited to, the signature's p-value. CO can be chosenby the artisan based on his or her specific constraints. For example, ifcost is not a critical factor and extremely high levels of sensitivityand specificity are desired, CO can be set very low such that onlytrivial increases in predictive power are disregarded. On the otherhand, if cost is decisive and moderate levels of sensitivity andspecificity are acceptable, CO can be set higher such that onlysignificant increases in predictive power warrant increasing the numberof genes in the signature.

Additionally, a skilled person would recognize that individual panelsmay be combined to generate additional panels according to thisdisclosure, and that combining two panels with acceptable predictivepower will result in a combined panel with acceptable predictive power.Additionally, a skilled person would recognize that while individualgenes are described herein as belonging to certain groups (i.e. CellCycle Genes, immune genes, etc.), all panels and genes disclosed hereinare unified by their common ability to aid in determining geneexpression, and treating and diagnosing melanoma.

CCP Genes

The present invention is based in part on the discovery that theexpression levels of CCP genes in a sample from a patient suspected ofhaving melanoma predict whether the patient will be diagnosed withmelanoma, and further that other genes, add significant prediction powerwhen combined with CCP genes (“CCGs”).

“Cell-cycle gene” and “CCG” herein refer to a gene whose expressionlevel closely tracks the progression of the cell through the cell-cycle.See, e.g., Whitfield et al., Mol. Biol. Cell (2002) 13:1977-2000. Theterm “cell-cycle progression” or “CCP” will also be used in thisapplication and will generally be interchangeable with CCG (i.e., a CCPgene is a CCG; a CCP score is a CCG score). More specifically, CCGs showperiodic increases and decreases in expression that coincide withcertain phases of the cell cycle—e.g., STK15 and PLK show peakexpression at G2/M. Id. Often CCGs have clear, recognized cell-cyclerelated function—e.g., in DNA synthesis or repair, in chromosomecondensation, in cell-division, etc. However, some CCGs have expressionlevels that track the cell-cycle without having an obvious, direct rolein the cell-cycle—e.g., UBE2S encodes a ubiquitin-conjugating enzyme,yet its expression closely tracks the cell-cycle. Thus a CCG accordingto the present invention need not have a recognized role in thecell-cycle. Exemplary CCGs are listed in Tables 1, 2, 3, 5, 6, 7, 8 & 9.A fuller discussion of CCGs, including an extensive (though notexhaustive) list of CCGs, can be found in International Application No.PCT/US2010/020397 (pub. no. WO/2010/080933) (see, e.g., Table 1 inWO/2010/080933). International Application No. PCT/US2010/020397 (pub.no. WO/2010/080933 (see also corresponding U.S. application Ser. No.13/177,887)) and International Application No. PCT/US2011/043228 (pubno. WO/2012/006447 (see also related U.S. application Ser. No.13/178,380)) and their contents are hereby incorporated by reference intheir entirety.

Whether a particular gene is a CCG may be determined by any techniqueknown in the art, including those taught in Whitfield et al., Mol. Biol.Cell (2002) 13:1977-2000; Whitfield et al., Mol. Cell. Biol. (2000)20:4188-4198; WO/2010/080933 (¶ [0039]). All of the CCGs in Table 1below form a panel of CCGs (“Panel A”). As will be shown detailthroughout this document, individual CCGs (e.g., CCGs in Table 1) andsubsets of these genes can also be used.

TABLE 1 Entrez RefSeq Gene Symbol GeneID ABI Assay ID Accession Nos.APOBEC3B* 9582 Hs00358981_m1 NM_004900.3 ASF1B* 55723 Hs00216780_m1NM_018154.2 ASPM* 259266 Hs00411505_m1 NM_018136.4 ATAD2* 29028Hs00204205_m1 NM_014109.3 BIRC5* 332 Hs00153353_m1; NM_001012271.1;Hs03043576_m1 NM_001012270.1; NM_001168.2 BLM* 641 Hs00172060_m1NM_000057.2 BUB1 699 Hs00177821_m1 NM_004336.3 BUB1B* 701 Hs01084828_m1NM_001211.5 C12orf48* 55010 Hs00215575_m1 NM_017915.2 C18orf24/ 220134Hs00536843_m1 NM_145060.3; SKA1*# NM_001039535.2 C1orf135* 79000Hs00225211_m1 NM_024037.1 C21orf45* 54069 Hs00219050_m1 NM_018944.2CCDC99* 54908 Hs00215019_m1 NM_017785.4 CCNA2* 890 Hs00153138_m1NM_001237.3 CCNB1* 891 Hs00259126_m1 NM_031966.2 CCNB2* 9133Hs00270424_m1 NM_004701.2 CCNE1* 898 Hs01026536_m1 NM_001238.1;NM_057182.1 CDC2* 983 Hs00364293_m1 NM_033379.3; NM_001130829.1;NM_001786.3 CDC20* 991 Hs03004916_g1 NM_001255.2 CDC45L* 8318Hs00185895_m1 NM_003504.3 CDC6* 990 Hs00154374_m1 NM_001254.3 CDCA3*83461 Hs00229905_m1 NM_031299.4 CDCA8* 55143 Hs00983655_m1 NM_018101.2CDKN3* 1033 Hs00193192_m1 NM_001130851.1; NM_005192.3 CDT1* 81620Hs00368864_m1 NM_030928.3 CENPA 1058 Hs00156455_m1 NM_001042426.1;NM_001809.3 CENPE* 1062 Hs00156507_m1 NM_001813.2 CENPF*# 1063Hs00193201_m1 NM_016343.3 CENPI* 2491 Hs00198791_m1 NM_006733.2 CENPM*79019 Hs00608780_m1 NM_024053.3 CENPN* 55839 Hs00218401_m1 NM_018455.4;NM_001100624.1; NM_001100625.1 CEP55*# 55165 Hs00216688_m1 NM_018131.4;NM_001127182.1 CHEK1* 1111 Hs00967506_m1 NM_001114121.1; NM_001114122.1;NM_001274.4 CKAP2* 26586 Hs00217068_m1 NM_018204.3; NM_001098525.1CKS1B* 1163 Hs01029137_g1 NM_001826.2 CKS2* 1164 Hs01048812_g1NM_001827.1 CTPS* 1503 Hs01041851_m1 NM_001905.2 CTSL2* 1515Hs00952036_m1 NM_001333.2 DBF4* 10926 Hs00272696_m1 NM_006716.3 DDX39*10212 Hs00271794_m1 NM_005804.2 DLGAP5/ 9787 Hs00207323_m1 NM_014750.3DLG7*# DONSON* 29980 Hs00375083_m1 NM_017613.2 DSN1* 79980 Hs00227760_m1NM_024918.2 DTL*# 51514 Hs00978565_m1 NM_016448.2 E2F8* 79733Hs00226635_m1 NM_024680.2 ECT2* 1894 Hs00216455_m1 NM_018098.4 ESPL1*9700 Hs00202246_m1 NM_012291.4 EXO1* 9156 Hs00243513_m1 NM_130398.2;NM_003686.3; NM_006027.3 EZH2* 2146 Hs00544830_m1 NM_152998.1;NM_004456.3 FANCI* 55215 Hs00289551_m1 NM_018193.2; NM_001113378.1FBXO5* 26271 Hs03070834_m1 NM_001142522.1; NM_012177.3 FOXM1*# 2305Hs01073586_m1 NM_202003.1; NM_202002.1; NM_021953.2 GINS1* 9837Hs00221421_m1 NM_021067.3 GMPS* 8833 Hs00269500_m1 NM_003875.2 GPSM2*29899 Hs00203271_m1 NM_013296.4 GTSE1* 51512 Hs00212681_m1 NM_016426.5H2AFX* 3014 Hs00266783_s1 NM_002105.2 HMMR* 3161 Hs00234864_m1NM_001142556.1; NM_001142557.1; NM_012484.2; NM_012485.2 HN1* 51155Hs00602957_m1 NM_001002033.1; NM_001002032.1; NM_016185.2 KIAA0101* 9768Hs00207134_m1 NM_014736.4 KIF11* 3832 Hs00189698_m1 NM_004523.3 KIF15*56992 Hs00173349_m1 NM_020242.2 KIF18A* 81930 Hs01015428_m1 NM_031217.3KIF20A* 10112 Hs00993573_m1 NM_005733.2 KIF20B/ 9585 Hs01027505_m1NM_016195.2 MPHOSPH1* K1F23* 9493 Hs00370852_m1 NM_138555.1; NM_004856.4KIF2C* 11004 Hs00199232_m1 NM_006845.3 KIF4A* 24137 Hs01020169_m1NM_012310.3 KIFC1* 3833 Hs00954801_m1 NM_002263.3 KPNA2 3838Hs00818252_g1 NM_002266.2 LMNB2* 84823 Hs00383326_m1 NM_032737.2 MAD2L14085 Hs01554513_g1 NM_002358.3 MCAM* 4162 Hs00174838_m1 NM_006500.2MCM10*# 55388 Hs00960349_m1 NM_018518.3; NM_182751.1 MCM2* 4171Hs00170472_m1 NM_004526.2 MCM4* 4173 Hs00381539_m1 NM_005914.2;NM_182746.1 MCM6* 4175 Hs00195504_m1 NM_005915.4 MCM7* 4176Hs01097212_m1 NM_005916.3; NM_182776.1 MELK 9833 Hs00207681_m1NM_014791.2 MKI67* 4288 Hs00606991_m1 NM_002417.3 MYBL2* 4605Hs00231158_m1 NM_002466.2 NCAPD2* 9918 Hs00274505_m1 NM_014865.3 NCAPG*64151 Hs00254617_m1 NM_022346.3 NCAPG2* 54892 Hs00375141_m1 NM_017760.5NCAPH* 23397 Hs01010752_m1 NM_015341.3 NDC80* 10403 Hs00196101_m1NM_006101.2 NEK2* 4751 Hs00601227_mH NM_002497.2 NUSAP1* 51203Hs01006195_m1 NM_018454.6; NM_001129897.1; NM_016359.3 OIP5* 11339Hs00299079_m1 NM_007280.1 ORC6L* 23594 Hs00204876_m1 NM_014321.2 PAICS*10606 Hs00272390_m1 NM_001079524.1; NM_001079525.1; NM_006452.3 PBK*#55872 Hs00218544_m1 NM_018492.2 PCNA* 5111 Hs00427214_g1 NM_182649.1;NM_002592.2 PDSS1* 23590 Hs00372008_m1 NM_014317.3 PLK1*# 5347Hs00153444_m1 NM_005030.3 PLK4* 10733 Hs00179514_m1 NM_014264.3 POLE2*5427 Hs00160277_m1 NM_002692.2 PRC1* 9055 Hs00187740_m1 NM_199413.1;NM_199414.1; NM_003981.2 PSMA7* 5688 Hs00895424_m1 NM_002792.2 PSRC1*84722 Hs00364137_m1 NM_032636.6; NM_001005290.2; NM_001032290.1;NM_001032291.1 PTTG1* 9232 Hs00851754_u1 NM_004219.2 RACGAP1* 29127Hs00374747_m1 NM_013277.3 RAD51* 5888 Hs00153418_m1 NM_133487.2;NM_002875.3 RAD51AP1* 10635 Hs01548891_m1 NM_001130862.1; NM_006479.4RAD54B* 25788 Hs00610716_m1 NM_012415.2 RAD54L* 8438 Hs00269177_m1NM_001142548.1; NM_003579.3 RFC2* 5982 Hs00945948_m1 NM_181471.1;NM_002914.3 RFC4* 5984 Hs00427469_m1 NM_181573.2; NM_002916.3 RFC5* 5985Hs00738859_m1 NM_181578.2; NM_001130112.1; NM_001130113.1; NM_007370.4RNASEH2A* 10535 Hs00197370_m1 NM_006397.2 RRM2*# 6241 Hs00357247_g1NM_001034.2 SHCBP1* 79801 Hs00226915_m1 NM_024745.4 SMC2* 10592Hs00197593_m1 NM_001042550.1; NM_001042551.1; NM_006444.2 SPAG5* 10615Hs00197708_m1 NM_006461.3 SPC25* 57405 Hs00221100_m1 NM_020675.3 STIL*6491 Hs00161700_m1 NM_001048166.1; NM_003035.2 STMN1* 3925Hs00606370_m1; NM_005563.3; Hs01033129_m1 NM_203399.1 TACC3* 10460Hs00170751_m1 NM_006342.1 TIMELESS* 8914 Hs01086966_m1 NM_003920.2 TK1*7083 Hs01062125_m1 NM_003258.4 TOP2A* 7153 Hs00172214_m1 NM_001067.2TPX2* 22974 Hs00201616_m1 NM_012112.4 TRIP13* 9319 Hs01020073_m1NM_004237.2 TTK* 7272 Hs00177412_m1 NM_003318.3 TUBA1C* 84790Hs00733770_m1 NM_032704.3 TYMS* 7298 Hs00426591_m1 NM_001071.2 UBE2C11065 Hs00964100_g1 NM_181799.1; NM_181800.1; NM_181801.1; NM_181802.1;NM_181803.1; NM_007019.2 UBE2S 27338 Hs00819350_m1 NM_014501.2 VRK1*7443 Hs00177470_m1 NM_003384.2 ZWILCH* 55055 Hs01555249_m1 NM_017975.3;NR_003105.1 ZWINT* 11130 Hs00199952_m1 NM_032997.2; NM_001005413.1;NM_007057.3 *124-gene subset of CCGs (“Panel B”). #10-gene subset ofCCGs (Panel C). ABI Assay ID means the catalogue ID number for the geneexpression assay commercially available from Applied Biosystems Inc.(Foster City, CA) for the particular gene.

Accordingly, in a first aspect of the present disclosure, panels ofgenes comprising CCGs for use in determining gene expression, and fordiagnosing and treating melanoma are disclosed. In some embodiments, useof panels comprising CCGs comprises determining expression of the CCGsin a sample from an individual or patient.

Gene expression can be determined either at the RNA level (i.e., mRNA ornoncoding RNA (ncRNA)) (e.g., miRNA, tRNA, rRNA, snoRNA, siRNA andpiRNA) or at the protein level. Measuring gene expression at the mRNAlevel includes measuring levels of cDNA corresponding to mRNA. Levels ofproteins in a sample can be determined by any known techniques in theart, e.g., HPLC, mass spectrometry, or using antibodies specific toselected proteins (e.g., IHC, ELISA, etc.).

In one embodiment, the amount of RNA transcribed from the panel of genesincluding test genes is measured in the sample. In addition, the amountof RNA of one or more housekeeping genes in the sample is also measured,and used to normalize or calibrate the expression of the test genes. Theterms “normalizing genes” and “housekeeping genes” are defined hereinbelow.

In any embodiment of the invention involving a “plurality of testgenes,” the plurality of test genes may include at least 2, 3 or 4cell-cycle genes, which constitute at least 50%, 75% or 80% of theplurality of test genes, and in some embodiments 100% of the pluralityof test genes. In some embodiments, the plurality of test genes includesat least 5, 6, 7, or at least 8 cell-cycle genes, which constitute atleast 20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of theplurality of test genes, and in some embodiments 100% of the pluralityof test genes. As will be clear from the context of this document, apanel of genes is a plurality of genes. Typically these genes areassayed together in one or more samples from a patient.

In some other embodiments, the plurality of test genes includes at least8, 10, 12, 15, 20, 25 or 30 cell-cycle genes, which constitute at least20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality oftest genes, and preferably 100% of the plurality of test genes.

As will be apparent to a skilled artisan apprised of the presentinvention and the disclosure herein, “sample” means any biologicalsample containing one or more suspected melanoma cells, or one or moreRNA or protein derived from suspected melanoma cells, and obtained froma patient. For example, a tissue sample obtained from a mole or nevus isa useful sample in the present invention. The tissue sample can be anFFPE sample, or fresh frozen sample, and preferably contain largely thesuspect cells. A single cell from a patient's suspected melanoma is alsoa useful sample. Such a cell can be obtained directly from the patient'sskin, or purified from the patient's bodily fluid (e.g., blood, urine).Thus, a bodily fluid such as blood, urine, sputum and saliva containingone or more suspected to be cancerous cells, or mole or nevus-derivedRNA or proteins, can also be useful as a sample for purposes ofpracticing the present invention.

Those skilled in the art are familiar with various techniques fordetermining the status of a gene or protein in a tissue or cell sampleincluding, but not limited to, microarray analysis (e.g., for assayingmRNA or microRNA expression, copy number, etc.), quantitative real-timePCR™ (“qRT-PCR™”, e.g., TaqMan™), immunoanalysis (e.g., ELISA,immunohistochemistry), etc. The activity level of a polypeptide encodedby a gene may be used in much the same way as the expression level ofthe gene or polypeptide. Often higher activity levels indicate higherexpression levels and while lower activity levels indicate lowerexpression levels. Thus, in some embodiments, the invention provides anyof the methods discussed above, wherein the activity level of apolypeptide encoded by the CCG is determined rather than or in additionto the expression level of the CCG. Those skilled in the art arefamiliar with techniques for measuring the activity of various suchproteins, including those encoded by the genes listed in Table 1. Themethods of the invention may be practiced independent of the particulartechnique used.

In some embodiments, the expression of one or more normalizing (oftencalled “housekeeping” or “housekeeper”) genes is also obtained for usein normalizing the expression of test genes. As used herein,“normalizing genes” referred to the genes whose expression is used tocalibrate or normalize the measured expression of the gene of interest(e.g., test genes). Importantly, the expression of normalizing genesshould be independent of cancer diagnosis, and the expression of thenormalizing genes is very similar among all the samples. Thenormalization ensures accurate comparison of expression of a test genebetween different samples. For this purpose, housekeeping genes known inthe art can be used. Housekeeping genes are well known in the art, withexamples including, but are not limited to, GUSB (glucuronidase, beta),HMBS (hydroxymethylbilane synthase), SDHA (succinate dehydrogenasecomplex, subunit A, flavoprotein), UBC (ubiquitin C), YWHAZ (tyrosine3-monooxygenase/tryptophan 5-monooxygenase activation protein, zetapolypeptide), MRFAP1, PSMA1, RPL13A, TXNL1, SLC25A3, RPS29, RPL8, PSMC1and RPL4. One or more housekeeping genes can be used. Preferably, atleast 2, 5, 10 or 15 housekeeping genes are used to provide a combinednormalizing gene set. The amount of gene expression of such normalizinggenes can be averaged, combined together by straight additions or by adefined algorithm. Some examples of particularly useful housekeepergenes for use in the methods and compositions of the invention includethose listed in Table 2 below.

TABLE 2 Gene Entrez Applied Biosystems RefSeq Symbol GeneID Assay IDAccession Nos. CLTC 1213 Hs00191535_m1 NM_004859.3 GUSB 2990Hs99999908_m1 NM_000181.2 HMBS 3145 Hs00609297_m1 NM_000190.3 MMADHC27249 Hs00739517_g1 NM_015702.2 MRFAP1* 93621 Hs00738144_g1 NM_033296.1PPP2CA 5515 Hs00427259_m1 NM_002715.2 PSMA1* 5682 Hs00267631_m1 PSMC1*5700 Hs02386942_g1 NM_002802.2 RPL13A* 23521 Hs03043885_g1 NM_012423.2RPL37 6167 Hs02340038_g1 NM_000997.4 RPL38 6169 Hs00605263_g1NM_000999.3 RPL4* 6124 Hs03044647_g1 NM_000968.2 RPL8* 6132Hs00361285_g1 NM_033301.1; NM_000973.3 RPS29* 6235 Hs03004310_g1NM_001030001.1; NM_001032.3 SDHA 6389 Hs00188166_m1 NM_004168.2 SLC25A3*6515 Hs00358082_m1 NM_213611.1; NM_002635.2; NM_005888.2 TXNL1* 9352Hs00355488_m1 NR_024546.1; NM_004786.2 UBA52 7311 Hs03004332_g1NM_001033930.1; NM_003333.3 UBC 7316 Hs00824723_m1 NM_021009.4 YWHAZ7534 Hs00237047_m1 NM_003406.3 *Subset of housekeeping genes used in,e.g., Example 3.

In the case of measuring RNA levels for the genes, one convenient andsensitive approach is real-time quantitative PCR™ (qPCR) assay,following a reverse transcription reaction. Typically, a cycle threshold(C_(t)) is determined for each test gene and each normalizing gene,i.e., the number of cycles at which the fluorescence from a qPCRreaction above background is detectable.

The overall expression of the one or more normalizing genes can berepresented by a “normalizing value” which can be generated by combiningthe expression of all normalizing genes, either weighted equally(straight addition or averaging) or by different predefinedcoefficients. For example, in a simplest manner, the normalizing valueC_(tH) can be the cycle threshold (C_(t)) of one single normalizinggene, or an average of the C_(t) values of 2 or more, preferably 10 ormore, or 15 or more normalizing genes, in which case, the predefinedcoefficient is 1/N, where N is the total number of normalizing genesused. Thus, C_(tH)=(C_(tH1)+C_(tH2)+ . . . C_(tHn))/N. As will beapparent to skilled artisans, depending on the normalizing genes used,and the weight desired to be given to each normalizing gene, anycoefficients (from 0/N to N/N) can be given to the normalizing genes inweighting the expression of such normalizing genes. That is,C_(tH)=xC_(tH1)+yC_(tH2)+ . . . zC_(tHn), wherein x+y+ . . . +z=1.

As discussed above, the methods of the invention generally involvedetermining the level of expression of a panel of CCGs. With modernhigh-throughput techniques, it is often possible to determine theexpression level of tens, hundreds or thousands of genes. Indeed, it ispossible to determine the level of expression of the entiretranscriptome (i.e., each transcribed sequence in the genome). Once sucha global assay has been performed, one may then informatically analyzeone or more subsets of transcripts (i.e., panels or, as often usedherein, pluralities of test genes). After measuring the expression ofhundreds or thousands of transcripts in a sample, for example, one mayanalyze (e.g., informatically) the expression of a panel or plurality oftest genes comprising primarily CCGs according to the present inventionby combining the expression level values of the individual test genes toobtain a test value.

As will be apparent to a skilled artisan, the test value provided in thepresent invention represents the overall expression level of theplurality of test genes composed substantially of cell-cycle genes. Inone embodiment, to provide a test value in the methods of the invention,the normalized expression for a test gene can be obtained by normalizingthe measured C_(t) for the test gene against the C_(tH), i.e.,ΔC_(t1)=(C_(t1)−C_(tH)). Thus, the test value representing the overallexpression of the plurality of test genes can be provided by combiningthe normalized expression of all test genes, either by straight additionor averaging (i.e., weighted equally) or by a different predefinedcoefficient. For example, the simplest approach is averaging thenormalized expression of all test genes: test value=(ΔC_(t1)+ΔC_(t2)+ .. . +ΔC_(tn))/n. As will be apparent to skilled artisans, depending onthe test genes used, different weight can also be given to differenttest genes in the present invention. In each case where this documentdiscloses using the expression of a plurality of genes (e.g.,“determining [in a sample from the patient] the expression of aplurality of test genes” or “correlating increased expression of saidplurality of test genes to an increased likelihood of having melanoma”),this includes in some embodiments using a test value representing,corresponding to or derived or calculated from the overall expression ofthis plurality of genes (e.g., “determining [in a sample from thepatient] a test value representing the expression of a plurality of testgenes” or “correlating an increased test value [or a test value abovesome reference value] (optionally representing the expression of saidplurality of test genes) to an increased likelihood of response”).

It has been determined that, once the CCP phenomenon reported herein isappreciated, the choice of individual CCGs for a test panel can often besomewhat arbitrary. In other words, many CCGs have been found to be verygood surrogates for each other. Thus any CCG (or panel of CCGs) can beused in the various embodiments of the invention. In other embodimentsof the invention, optimized CCGs are used. One way of assessing whetherparticular CCGs will serve well in the methods and compositions of theinvention is by assessing their correlation with the mean expression ofCCGs (e.g., all known CCGs, a specific set of CCGs, etc.). Those CCGsthat correlate particularly well with the mean are expected to performwell in assays of the invention, e.g., because these will reduce noisein the assay.

Thus, in some embodiments of each of the various aspects of theinvention the plurality of test genes comprises the top 2, 3, 4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 20, 25, 30, 35, 40 or more CCGs fromPanel A. In some embodiments of each of the various aspects of theinvention the plurality of test genes comprises the top 2, 3, 4, 5, 6,7, 8, 9 or 10 CCGs from Panel B. In some embodiments the plurality oftest genes comprises at least some number of CCGs (e.g., at least 3, 4,5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and thisplurality of CCGs comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9 or 10 ofthe genes in Panel B. In some embodiments the plurality of test genescomprises at least some number of CCGs (e.g., at least 3, 4, 5, 6, 7, 8,9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and this pluralityof CCGs comprises any two, three, four, five, six, seven, eight, nine,or ten of gene numbers 1 & 2, 1 to 3, 1 to 4, 1 to 5, 1 to 6, 1 to 7, 1to 8, 1 to 9, or 1 to 10 of any of the genes in Panel B (based on orderof appearance in Table 1). In some embodiments the plurality of testgenes comprises at least some number of CCGs (e.g., at least 3, 4, 5, 6,7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and thisplurality of CCGs comprises any one, two, three, four, five, six, seven,eight, or nine or all of gene numbers 2 & 3, 2 to 4, 2 to 5, 2 to 6, 2to 7, 2 to 8, 2 to 9, or 2 to 10 of any of the genes in Panel B (basedon order of appearance in Table 1). In some embodiments the plurality oftest genes comprises at least some number of CCGs (e.g., at least 3, 4,5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and thisplurality of CCGs comprises any one, two, three, four, five, six, seven,or eight or all of gene numbers 3 & 4, 3 to 5, 3 to 6, 3 to 7, 3 to 8, 3to 9, or 3 to 10 of any of the genes in Panel B (based on order ofappearance in Table 1). In some embodiments the plurality of test genescomprises at least some number of CCGs (e.g., at least 3, 4, 5, 6, 7, 8,9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more CCGs) and this pluralityof CCGs comprises any one, two, three, four, five, six, or seven or allof gene numbers 4 & 5, 4 to 6, 4 to 7, 4 to 8, 4 to 9, or 4 to 10 of anyof the genes in Panel B (based on order of appearance in Table 1).

In another embodiment, the plurality of CCGs or panel of CCGs comprisesany set of genes from Table WW.

Immune and Additional Genes

It has additionally surprisingly been discovered that panels of immunegenes are diagnostic for melanoma. Accordingly, in another aspect of thepresent disclosure, panels of genes comprising immune genes for use indetermining gene expression, and for diagnosing and treating melanomaare disclosed. In some embodiments, use of panels comprising immunegenes comprises determining expression of the immune genes in a samplefrom an individual or patient.

“Immune gene” herein refers to a gene associated with or expressed byone or more leukocytes. In particular embodiments, immune genes comprisegenes associated with or expressed by lymphocytes. In some embodiments,the immune genes comprise genes expressed by activated lymphocytes. Insome embodiments, immune genes comprise genes expressed by T cells. Insome embodiments, immune genes comprise genes expressed by activated Tcells. In some embodiments, immune genes comprise the immune genesidentified in Table 3, below.

TABLE 3 Entrez RefSeq Gene Name Gene ID Ensembl Gene ID Nos. AccessionARPC2 10109 ENSG00000163466 NM_005731; NM_152862 BCL2A1* 597ENSG00000140379 NM_004049 CCL19# 6363 ENSG00000172724 NM_006274 CCL3*#6348 ENSG00000006075| NM_002983; ENSG00000136826 NM_004235CCL5*#{circumflex over ( )} 6352 ENSG00000161570 NM_002985CD38*#{circumflex over ( )} 952 ENSG00000004468 NM_001775 CFH*# 3075ENSG00000000971 NM_000186; NM_001014975.1 CXCL10*#{circumflex over ( )}3627 ENSG00000169245 NM_001565.2 CXCL12# 6387 ENSG00000107562|NM_000609.4; ENSG00000126214 NM_001033886; NM_199168; NM_005552;NM_182923.3 CXCL13*# 10563 ENSG00000156234 NM_006419 CXCL9*#{circumflexover ( )} 4283 ENSG00000138755 NM_002416 FABP7* 2173 ENSG00000113805NM_020872 FN1* 2335 ENSG00000115414| NM_002026; ENSG00000197721NM_054034.2; NM_212474; NM_212476.1; NM_212482.1; NM_175710.1 GDF15 9518HCLS1*# 3059 ENSG00000113070| NM_001945; ENSG00000180353 NM_005335 HEY1*23462 ENSG00000164683 NM_001040708.1; NM_012258 HLA- 3108ENSG00000204257| NM_006120 DMA*# ENSG00000206229| ENSG00000206293HLA-DPA1# 3113 HLA-DPB1# 3115 ENSG00000112242| NM_001949;ENSG00000168383 NM_002121 HLA-DRA*# 3122 ENSG00000143768| NM_003240;ENSG00000204287| NM_019111 ENSG00000206243 HLA-E# 3133 ENSG00000204592NM_005516 IFI6*# 2537 ENSG00000126709| NM_002038; ENSG00000135047NM_022872; NM_022873; NM_001912; NM_145918 IGHM# 3507 IGJ*# 3512ENSG00000132465| NM_144646; ENSG00000182197 NM_000127 IGLL5/CKA100423062 P2# IRF1*#{circumflex over ( )} 3659 IRF4# 3662ENSG00000137265 NM_002460 ITGB2*# 3689 ENSG00000160255 NM_000211 KRT15*3866 ENSG00000171346 NM_002275 LCP2*#{circumflex over ( )} 3937ENSG00000043462 NM_005565.3 NCOA3 8202 ENSG00000124151 NM_006534;NM_181659 NR4A1 3164 ENSG00000107223| NM_003792; ENSG00000123358NM_153200; NM_002135; NM_173157; NM_173158 PECAM1*# 5175ENSG00000173744| NM_004504; ENSG00000198802 NM_000442.3 PHACTR1* 221692ENSG00000112137 NM_030948.1 PHIP 55023 ENSG00000146247 NM_017934 POU5F15460 ENSG00000204531| NM_002701; ENSG00000206349| NM_203289.3ENSG00000206454 PRAME* 23532 ENSG00000185686 NM_006115; NM_206953;NM_206954; NM_206955; NM_206956 PTN* 5764 PTPN22*# 26191 ENSG00000134242NM_012411; NM_015967 PTPRC*#{circumflex over ( )} 5788 ENSG00000081237NM_002838; NM_080921; NM_080922; NM_080923.2 RGS1* 5996 ENSG00000090104NM_002922 S100A9* 6280 ENSG00000163220 NM_002965 SELL* 6402ENSG00000188404 NM_000655.3 SERPINB4# 6318 ENSG00000057149| NM_002974;ENSG00000068796 NM_004520 SOCS3# 9021 ENSG00000184557 NM_003955 SPP1*6696 WIF1 11197 ENSG00000125285 NM_007084 WNT2 7472 ENSG00000105989NM_003391 *Panel of 30 mixed genes (Panel D); #panel of 28 immune genes(Panel E); {circumflex over ( )}panel of 8 immune genes (Panel F)

Gene expression of immune genes can be determined as described abovewith respect to CCGs. In one embodiment, the amount of RNA transcribedfrom the panel of genes including immune genes is measured in thesample. In addition, the amount of RNA of one or more housekeeping genesin the sample is also measured, and used to normalize or calibrate theexpression of the test genes. The terms “normalizing genes” and“housekeeping genes” are defined above.

In any embodiment of the invention involving a “plurality of testgenes,” the plurality of test genes may include at least 2, 3 or 4immune genes, which constitute at least 50%, 75% or 80% of the pluralityof immune genes, and in some embodiments 100% of the plurality of immunegenes. In some embodiments, the plurality of immune genes includes atleast 5, 6, 7, or at least 8 cell-cycle genes, which constitute at least20%, 25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality ofimmune genes, and in some embodiments 100% of the plurality of immunegenes. As will be clear from the context of this document, a panel ofgenes is a plurality of genes. Typically these genes are assayedtogether in one or more samples from a patient.

In some other embodiments, the plurality of immune genes includes atleast 8, 10, 12, 15, 20, 25 or 30 immune, which constitute at least 20%,25%, 30%, 40%, 50%, 60%, 70%, 75%, 80% or 90% of the plurality of immunegenes, and preferably 100% of the plurality of immune genes.

The sample used to determine the expression of immune genes may be anysample as described above for CCGs.

In the case of measuring RNA levels for the immune genes, real-timequantitative PCR™ (qPCR) assay with normalized values, as describedabove, may be used.

As discussed above, some embodiments of the methods disclosed generallyinvolve determining the level of expression of a panel comprising immunegenes. With modern high-throughput techniques, it is often possible todetermine the expression level of tens, hundreds or thousands of genes.Indeed, it is possible to determine the level of expression of theentire transcriptome (i.e., each transcribed sequence in the genome).Once such a global assay has been performed, one may then informaticallyanalyze one or more subsets of transcripts (i.e., panels or, as oftenused herein, pluralities of test genes). After measuring the expressionof hundreds or thousands of transcripts in a sample, for example, onemay analyze (e.g., informatically) the expression of a panel orplurality of test genes comprising primarily immune genes according tothe present invention by combining the expression level values of theindividual test genes to obtain a test value.

Thus, in some embodiments of each of the various aspects of theinvention the plurality of test genes comprises any 2, 3, 4, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, 20, 25, or 28 Immune genes from Panel E. Insome embodiments of each of the various aspects of the invention theplurality of test genes comprises 2, 3, 4, 5, 6, 7, 8 immune genes fromPanel F. In some embodiments the plurality of test genes comprises atleast some number of immune genes (e.g., at least 2, 3, 4, 5, 6, 7, 8,9, 10, 15, 20, 25, 30, 35, 40, 45, 50 or more immune genes) and thisplurality of immune genes comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 15, 20, 25, or 28 of the genes in Panel E, or 2, 3, 4, 5, 6, 7, 8, 9or 10 immune genes from Panel F.

It has also been found that additional genes may be diagnostic formelanoma. Without being bound by theory, these additional genes arebelieved to be non-CCG and non-immune genes, and comprise ARPC2, BCL2A1,FABP7, FN1, GDF15, HEY1, KRT15, NCOA3, NR4A1, PHACTR1, PHIP, POU5F1,PRAME, PTN, RGS1, S100A9, SELL, SPP1, WIF1, and WNT2 (Panel G).Accordingly, in another aspect of the present disclosure, panels ofgenes comprising these additional genes are disclosed for use indetermining gene expression, and for diagnosing and treating melanoma.

In one embodiment, the panel comprising these additional genes: BCL2A1,FABP7, FN1, HEY1, KRT15, PHACTR1, PRAME, PTN, RGS1, S100A9, SELL, andSPP1 (Panel H). In another embodiment, the panel comprising additionalgenes comprises PRAME and S100A9.

In additional embodiments, this disclosure provides for mixed panels ofgenes which are useful in determining gene expression, and fordiagnosing and treating melanoma. These mixed panels may comprise immunegenes and CCGs, or immune genes and genes from Panel G or H, or CCGs andgenes from panel G or H. In one embodiment, the mixed panel comprisesone or more CCGs, one or more immune genes, and one or more additionalgenes from panel G. In one embodiment, the mixed panel comprises PanelD. In another embodiment, the mixed panel comprises PRAME, S100A9 andthe genes of panel F.

In another embodiment, the mixed panel comprises one or more CCGs, oneor more immune genes, and one or more additional genes from panel H. Inone embodiment of a mixed panel, the mixed panel (Panel I) comprises thegenes from Panel C and the genes from Panel D.

In one embodiment of a mixed panel, the mixed panel) comprises S100A9and/or S100A9-related genes. The S100A9 related genes can include genesthat have highly correlated expression compared to S100A9. TheseS100A9-related genes may include genes that are closely clustered withS100A9 on chromosome 1. These S100A9-related genes may also includegenes that have similar transcription control as S100A9. TheS100A9-related proteins may also be part of the same biological pathway.The S100A9-related genes may also code for proteins that interact withthe protein coded by S100A9. As a non-limiting example, the mixed panelmay comprise S100A9, S100A7, S100A8, S100A12, PI3, S100A10, and S100A14(Panel J). As a non-limiting example, the mixed panel may compriseS100A9, S100A7, S100A8, S100A12, and PI3 (Panel L).

In an alternate embodiment, the mixed panel comprises PRAME. In anotherembodiment, the mixed panel comprises S100A9. In yet another embodiment,the mixed panel comprises CCL5, CD38, CXCL10, CXCL9, IRF1, LCP2, PTPN22,or PTPRC. In other embodiments the mixed panel comprises S100A7, S100A8,S100A12, PI3, S100A10, and S100A14. In some embodiments, the mixed panelcomprises S100A9, S100A7, S100A8, S100A12, and PI3. Thus, in someembodiments of each of the various aspects of the invention the panel ofmixed genes comprises any 2, 3, 4, 5, 6, or 7 S100A9-related genes fromPanel J. In other embodiments of each of the various aspects of theinvention the panel of mixed genes comprises any 2, 3, 4, 5, 6, or 7S100A9-related genes from Panel L.

Thus, in some embodiments of each of the various aspects of theinvention the panel of mixed genes comprises PRAME, at least one of thegenes of Panel J, and at least one of the genes of panel F. In someembodiments, the mixed panel comprises PRAME, S100A9, S100A7, S100A8,S100A12, S100A10, S100A14, PI3, CCL5, CD38, CXCL10, CXCL9, IRF1, LCP2,PTPN22, and PTPRC.

Thus, in some embodiments of each of the various aspects of theinvention the panel of mixed genes comprises PRAME, at least one of thegenes of Panel L, and at least one of the genes of panel F. In someembodiments, the mixed panel comprises PRAME, S100A9, S100A7, S100A8,S100A12, PI3, CCL5, CD38, CXCL10, CXCL9, IRF1, LCP2, PTPN22, and PTPRC.

Thus, in some embodiments of each of the various aspects of theinvention the panel of mixed genes comprises PRAME, S100A9, and at least1, 2, 3, 4, 5, 6, 7, or 8 genes of panel F. In some embodiments, thepanel of mixed genes comprises PRAME and at least 1, 2, 3, 4, 5, 6, or 7genes of Panel J, and at least 1, 2, 3, 4, 5, 6, 7, or 8 genes of panelF. In some embodiments, the panel of mixed genes comprises PRAME,S100A9, and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,16, 17, 18, 19, or 20 genes of panel E. In some embodiments, the panelof mixed genes comprises PRAME, and at least 1, 2, 3, 4, 5, 6, or 7genes of Panel J, and at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, or 20 genes of Panel E.

Thus, in some embodiments of each of the various aspects of theinvention the panel of mixed genes comprises at least 1, 2, 3, 4, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,26, 27, 28, 29, or 30 genes of Panel D, and S100A9, and at least 1, 2,3, 4, 5, 6, 7, or 8 genes of panel F. In some embodiments, the panel ofmixed genes comprises at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or30 genes of Panel D and at least 1, 2, 3, 4, 5, 6, or 7 genes of PanelJ, and at least 1, 2, 3, 4, 5, 6, 7, or 8 genes of panel F. In someembodiments, the panel of mixed genes comprises at least 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,25, 26, 27, 28, 29, or 30 genes of Panel D, S100A9, and at least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genesof panel E. In some embodiments, the panel of mixed genes comprises atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,20, 21, 22, 23, 24, 25, 26, 27, 28, 29, or 30 genes of Panel D, and atleast 1, 2, 3, 4, 5, 6, or 7 genes of Panel J, and at least 1, 2, 3, 4,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes ofPanel E.

Thus, in some embodiments of each of the various aspects of theinvention the panel of mixed genes comprises at least at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes ofPanel G, and S100A9, and at least 1, 2, 3, 4, 5, 6, 7, or 8 genes ofpanel F. In some embodiments, the panel of mixed genes comprises atleast at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,17, 18, 19, or 20 genes of Panel G, and at least 1, 2, 3, 4, 5, 6, or 7genes of Panel J, and at least 1, 2, 3, 4, 5, 6, 7, or 8 genes of panelF. In some embodiments, the panel of mixed genes comprises at least 1 atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,or 20 genes of Panel G, S100A9, and at least 1, 2, 3, 4, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes of panel E. In someembodiments, the panel of mixed genes comprises at least at least 1, 2,3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genesof Panel G, and at least 1, 2, 3, 4, 5, 6, or 7 genes of Panel J, and atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19,or 20 genes of Panel E.

Thus, in some embodiments of each of the various aspects of theinvention the panel of mixed genes comprises at least at least at least1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 genes of Panel H, and S100A9,and at least 1, 2, 3, 4, 5, 6, 7, or 8 genes of panel F. In someembodiments, the panel of mixed genes comprises at least at least atleast 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 genes of Panel H, and atleast 1, 2, 3, 4, 5, 6, or 7 genes of Panel J, and at least 1, 2, 3, 4,5, 6, 7, or 8 genes of panel F. In some embodiments, the panel of mixedgenes comprises at least 1 at least 1 at least 1, 2, 3, 4, 5, 6, 7, 8,9, 10, 11, or 12 genes of Panel H, S100A9, and at least 1, 2, 3, 4, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20 genes of panelE. In some embodiments, the panel of mixed genes comprises at least atleast at least 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 genes of PanelH, and at least 1, 2, 3, 4, 5, 6, or 7 genes of Panel J, and at least 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20genes of Panel E.

In one embodiment, the panel comprises any set of two genes from TableXX. In another embodiment, the panel comprises any set of three genesfrom Table YY. In another embodiment, the panel comprises any set offour genes from Table ZZ.

In one embodiment of a panel of housekeeper genes, the housekeeper panel(Panel K) comprises one or more genes for use in normalizing theexpression of test genes. Panel K can be made up of any gene whoseexpression is used to calibrate or normalize measured expression of thegene or genes of interest. Panel K can be made up of any housekeeping orhousekeeper genes known in the art. Examples of housekeeper genes thatcan be used in Panel K include CLTC, GUSB, HMBS, MMADHC, MRFAP1, PPP2CA,PSMA1, PSMC1, RPL13A, RPL37, RPL38, RPL4, RPL8, RPS29, SDHA, SLC25A3,TXNL1, UBA52, UBC, and YWHAZ. In some embodiments, the housekeeper genesused to normalize the expression of test genes can include at least 1,2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, or 20genes of Panel K.

Methods of Determining Gene Expression

Accordingly, in a first aspect of the present invention, a method isprovided for determining gene expression in a sample from a patient(e.g., one suspected of containing melanoma). Generally, the methodincludes at least the following steps: (1) obtaining a sample from apatient (e.g., one suspected of containing melanoma); (2) determiningthe expression of a panel of genes in the sample; and (3) providing atest value by (a) weighting the determined expression of each gene fromthe panel of genes with a predefined coefficient, and (b) combining theweighted expression of each gene from the panel of genes to provide saidtest value.

Weighting the expression of each gene from the panel of genes may beperformed individually for each gene, or genes may first be grouped andtheir normalized expression averaged or otherwise combined beforeweighting is performed. In some embodiments, genes are grouped based onwhether they provide independent information in separating nevi frommelanoma. In some examples, CCGs are grouped before weighting. In otherembodiments, immune genes are grouped before weighting. The skilledartisan will understand that in some embodiments, grouping may beconceptualized as a way of individually weighting each gene in thepre-defined group to arrive at an intermediate value, which intermediatevalue is weighted along with other individual gene expression values toobtain a final value. In some embodiments, multiple rounds of groupingmay be performed, resulting in multiple intermediate values, which maybe in turn grouped to obtain a final value.

In some embodiments, weighting coefficients are determined whichoptimize the contribution of each expression profile to the predictivevalue of any resulting test value. In some embodiments, genes whoseexpression is more highly correlated or anti-correlated with melanomareceive a larger weighting coefficient in order to maximize the overallpredictive power of any resulting test value. In some embodiments, geneswhose expression is correlated or anti-correlated with melanoma, butless correlated with the expression of other genes in the panel receivea larger weighting coefficient in order to maximize the overallpredictive power of any resulting test value. In some embodiments, geneswhose expression is significantly, moderately, or highly correlated maybe grouped.

In some embodiments, regression analyses are utilized to obtainappropriate weighting coefficients to maximize the predictive power of atest value. In some embodiments, linear regression is used to fitexpression levels to a model for providing test values which arediagnostic of melanoma. In other embodiments, logistic regression isused to determine weighting coefficients for expression levels ofindividual genes or groups of genes in a model for diagnosis ofmelanoma.

In some embodiments, weighting the expression of each gene comprisesgrouping immune genes, and then weighting the expression of immunegenes, PRAME and S100A9 to arrive at a test value which is diagnosticfor melanoma. In related embodiments, there are 8 immune genes. Inrelated embodiments, the immune genes comprise Panel F. In someembodiments, the weighting to arrive at a test value is as follows: testvalue=(A×PRAME)+(B×grouped immune)+(C×S100A9). In a related embodiment,A is 0.525, B is 0.677 and C is 0.357.

In some embodiments, weighting the expression of each gene comprisesgrouping immune genes, grouping S100A9-related genes and then weightingthe expression of immune genes, PRAME and the S100A9-related genes toarrive at a test value which is diagnostic for melanoma. In relatedembodiments, there are 8 immune genes. In related embodiments, theimmune genes comprise Panel F. In related embodiments, there are 7S100A9-related genes. In related embodiments, the S100A9-related genescomprise Panel J. In related embodiments, the S100A9-related genescomprise Panel L. In some embodiments, the weighting to arrive at a testvalue is as follows: test value=(A×PRAME)+(B×grouped immune)+(C×groupedS100A9-related). In a related embodiment, A is 1.149, B is 0.698 and Cis 0.922.

In some embodiments, weighting the expression of each gene comprisesgrouping immune genes, grouping S100A9-related genes and then weightingthe expression of immune genes, PRAME and the S100A9-related genes andthen adjusting by a linear scale factor to arrive at a test value whichis diagnostic for melanoma. The linear scale factor adjusts the cutoffvalue so that the cutoff value is centered about zero. In relatedembodiments, there are 8 immune genes. In related embodiments, theimmune genes comprise Panel F. In related embodiments, there are 7S100A9-related genes. In related embodiments, the S100A9-related genescomprise Panel J. In separate related embodiments, the S100A9-relatedgenes comprise Panel L. In some embodiments, the weighting to arrive ata test value is as follows: test value=(A×PRAME)+(B×groupedimmune)+(C×grouped S100A9-related)+D. In a related embodiment, A is1.149, B is 0.698, C is 0.922, and D is −0.334.

In some embodiments a test value derived from expression levels may becombined with non-expression parameters to arrive at a modified testvalue or score which is diagnostic for melanoma. In some embodiments,clinical factors may be combined with a test value derived fromexpression levels in order to provide a score which is diagnostic formelanoma. In related embodiments, clinical staging data may be weightedand combined with a test value based on expression to obtain a scorewhich is diagnostic for melanoma.

Methods of Diagnosing Melanoma

Provided herein are also methods of diagnosing melanoma. Generally, amethod is provided for diagnosing melanoma, which comprises a)determining in a sample from an individual the expression of a panel ofgenes; b) comparing the expression of the panel of genes in the sampleto the expression of the panel of genes in one or more control samples;and c) diagnosing the individual with melanoma, or concluding that theindividual is likely to have melanoma, based at least in part on adifference between the expression of one or more genes of the panel ofgenes in the sample versus the one or more control samples.

The step of comparing the expression of the panel of genes may beperformed directly (i.e. obtaining an expression value for each gene inthe panel of genes in the sample and in the one or more control sample,and determining on a gene by gene basis if there is a significantdifference between the expression in the sample versus the one or morecontrols). Alternately, comparing the expression of the panel of genesin the sample to the expression in one or more control samples may beperformed implicitly. In some embodiments, implicit comparison isachieved by building a model based on the one or more control samplesand determining where the expression of the panel of genes in theindividual sample fits within the model. In one embodiment, implicitcomparison of the expression of the panel of genes in the sample to oneor more control samples comprises utilizing a pre-determined set ofweighting coefficients based on analysis of the one or more controlsamples to weight the expression of the panel of genes in the sample andarrive at a test value. In a related embodiment, the test value iscompared to a pre-determined cutoff value based on analysis of the oneor more control samples to achieve implicit comparison.

In some embodiments, the methods of diagnosis further comprisecommunicating that the individual is likely to have melanoma.

As used herein, “communicating” a particular piece of information meansto make such information known to another person or transfer suchinformation to a thing (e.g., a computer). In some methods of theinvention, a patient's diagnosis or likelihood of having melanoma iscommunicated. In some embodiments, the information used to arrive atsuch a diagnosis or likelihood prediction is communicated. Thiscommunication may be auditory (e.g., verbal), visual (e.g., written),electronic (e.g., data transferred from one computer system to another),etc. In some embodiments, communicating a diagnosis or likelihood ofmelanoma comprises generating a report that communicates the diagnosisor likelihood of melanoma. In some embodiments the report is a paperreport, an auditory report, or an electronic record. In some embodimentsthe report is displayed and/or stored on a computing device (e.g.,handheld device, desktop computer, smart device, website, etc.). In someembodiments the diagnosis or likelihood of melanoma is communicated to aphysician (e.g., a report communicating the classification is providedto the physician). In some embodiments the diagnosis or likelihood ofmelanoma is communicated to a patient (e.g., a report communicating theclassification is provided to the patient). Communicating a diagnosis orlikelihood of melanoma can also be accomplished by transferringinformation (e.g., data) embodying the classification to a servercomputer and allowing an intermediary or end-user to access suchinformation (e.g., by viewing the information as displayed from theserver, by downloading the information in the form of one or more filestransferred from the server to the intermediary or end-user's device,etc.).

Wherever an embodiment of the invention comprises concluding some fact(e.g., a patient's likelihood of having melanoma), this may include acomputer program concluding such fact, typically after performing analgorithm that applies information on the expression of the panel ofgenes in the sample, as described above.

In some embodiments, the method of diagnosis includes (1) obtaining asample from a patient suspected of having melanoma; (2) determining theexpression of a panel of genes in the sample including at least 2, 4, 6,8 or 10 cell-cycle genes, or at least 2, 4, 6 or 8 immune genes; and (3)providing a test value by (a) weighting the determined expression ofeach of a plurality of test genes selected from the panel of genes witha predefined coefficient, and (b) combining the weighted expression toprovide said test value, wherein at least 20%, 50%, at least 75% or atleast 90% of said plurality of test genes are cell-cycle genes, andwherein high expression (or increased expression or overexpression) ofthe plurality of test genes indicates a increase likelihood of havingmelanoma. In some embodiments, the method comprises at least one of thefollowing steps: (a) correlating high expression (or increasedexpression or overexpression) of the plurality of test genes to anincreased likelihood of having melanoma; (b) concluding that the patienthas an increased likelihood of having melanoma based at least in part onhigh expression (or increased expression or overexpression) of theplurality of test genes; or (c) communicating that the patient has anincreased likelihood of having melanoma based at least in part on highexpression (or increased expression or overexpression) of the pluralityof test genes.

In some embodiments, the expression levels measured in a sample are usedto derive or calculate a value or score, as described above. This valuemay be derived solely from expression levels or optionally derived froma combination of the expression value scores with other components(e.g., clinical staging, etc.) to give a potentially more comprehensivevalue/score. Thus, in every case where an embodiment of the inventiondescribed herein involves determining the status of a biomarker (e.g.,CCGS, immune genes or additional genes, as defines), related embodimentsinvolve deriving or calculating a value or score from the measuredstatus (e.g., expression score, or combined score).

In some such embodiments, multiple scores (e.g., expression test valueand clinical parameters, such as clinical staging) can be combined intoa more comprehensive score. Single component (e.g., CCG) or combinedtest scores for a particular patient can be compared to single componentor combined scores for reference populations, with differences betweentest and reference scores being correlated to or indicative of someclinical feature. Thus, in some embodiments the invention provides amethod of determining a melanoma diagnosis comprising (1) obtaining themeasured expression levels of a panel of genes in a sample from thepatient, (2) calculating a test value from these measured expressionlevels, (3) comparing said test value to a reference value calculatedfrom measured expression levels of the panel of genes in a referencepopulation of patients, and (4)(a) correlating a test value greater thanthe reference value to a diagnosis of melanoma or (4)(b) correlating atest value equal to or less than the reference value to a benigndiagnosis.

In some such embodiments the test value is calculated by averaging themeasured expression of the panel genes (as discussed below). In someembodiments the test value is calculated by weighting each of the panelof genes in a particular way, as describe above.

In some embodiments the combined score includes CCP score as previouslydefined. In some embodiments, the combined score includes an immunescore as demonstrated in the Examples. In some embodiments the immunescore is an average of the expression of the genes in an immune genepanel. In some embodiments, the immune score is an average of theexpression of the genes in Table 30. The immune score may be any valueused to represent the expression of one or more immune genes asdescribed herein. In some embodiments the immune score is an average ofthe expression of the genes in an immune gene panel. In someembodiments, the immune score is an average of the expression of thegenes in Panel D. In some embodiments the immune score is an average ofthe expression of the genes in an immune gene panel. In someembodiments, the immune score is an average of the expression of thegenes in Panel E. In some embodiments the immune score is an average ofthe expression of the genes in an immune gene panel. In someembodiments, the immune score is an average of the expression of thegenes in Panel F. A combined score may also include individual geneswith independent predictive value, and other non-expression basedclinical factors. CCP and immune scores can be a continuous numericvariable.

In some embodiments the combined score is calculated according to thefollowing formula:

$\begin{matrix}\left. {{{Combined}\mspace{14mu}{score}} = {{A*\left( {{CCP}\mspace{14mu}{score}} \right)} + {B*\left( {{immune}\mspace{14mu}{score}} \right)} + \left\{ {C*{additional}\mspace{14mu}{gene}\mspace{14mu} X\mspace{14mu}{expression}} \right) + {D*{additional}\mspace{14mu}{gene}\mspace{14mu} Y\mspace{14mu}{expression}\;\ldots}}} \right\} & (1)\end{matrix}$Where X and Y represent any diagnostic additional gene as describedherein, and the ellipsis indicates that extra additional genes, eachwith their own coefficient may be added.

Additionally, in some embodiments, the combined score is calculatedaccording to the following formula:

$\begin{matrix}\left. {{{Combined}\mspace{14mu}{score}} = {{B*\left( {{immune}\mspace{14mu}{score}} \right)} + \left\{ {C*{additional}\mspace{14mu}{gene}\mspace{14mu} X\mspace{14mu}{expression}} \right) + {D*{additional}\mspace{14mu}{gene}\mspace{14mu} Y\mspace{14mu}{expression}\;\ldots}}} \right\} & (2)\end{matrix}$Where X and Y represent any diagnostic additional gene as describedherein, and the ellipsis indicates that extra additional genes, eachwith their own coefficient may be added. In a related embodiment,additional gene X is PRAME and additional gene Y is S100A9.

Furthermore, in yet other embodiments, the combined score is calculatedaccording the following formula:

$\begin{matrix}{{{Combined}\mspace{14mu}{score}} = {{B*\left( {{immune}\mspace{14mu}{score}} \right)} + \left\{ {{C*\left( {{additional}\mspace{14mu}{gene}\mspace{14mu} X\mspace{14mu}{expression}} \right)} + {D*{additional}\mspace{14mu}{gene}\mspace{14mu} Y\mspace{14mu}{expression}\;\ldots}} \right\} + {{adjustment}\mspace{14mu}{factor}}}} & (3)\end{matrix}$Where X and Y represent any diagnostic additional gene as describedherein, and the ellipsis indicates that extra additional genes, eachwith their own coefficient may be added. The adjustment factorrepresents a scalar factor that can be used to adjust the linear score.For example in some embodiments, the adjustment factor can adjust thescore of a particular cutoff value such that the cutoff value iscentered at zero. In a related embodiment, additional gene X is PRAMEand additional gene Y is S100A9.

Furthermore, in yet other embodiments, the combined score is calculatedaccording the following formula:

$\begin{matrix}{{{Combined}\mspace{14mu}{score}} = {{B*\left( {{immune}\mspace{14mu}{score}} \right)} + \left\{ {{C*\left( {S\; 100\mspace{14mu}{Score}} \right)} + {D*{additional}\mspace{14mu}{gene}\mspace{14mu} Y\mspace{14mu}{expression}\;\ldots}} \right\} + {{adjustment}\mspace{14mu}{factor}}}} & (4)\end{matrix}$Where Y represents any diagnostic additional gene as described herein,and the ellipsis indicates that extra additional genes, each with theirown coefficient may be added. The adjustment factor represents a scalarfactor that can be used to adjust the linear score. For example in someembodiments, the adjustment factor can adjust the score of a particularcutoff value such that the cutoff value is centered at zero. The S100score may be any value used to represent the expression of one or moreS100A9 and/or S100A9 related genes as described herein. In someembodiments the S100 score is an average of the expression of the genesin an S100A9 and/or S100A9 related gene panel. In a related embodiment,the S100 score is an average of the expression of the genes in Panel J.In a related embodiment, the S100 score is an average of the expressionof the genes in Panel L. In a related embodiment, the S100 score is anaverage of the expression of S100A9, S100A7, S100A8, S100A12, PI3,S100A10, and S100A14. In a related embodiment, the S100 score is anaverage of the expression of S100A9, S100A7, S100A8, S100A12, and PI3.In a related embodiment, additional gene Y is PRAME.

In some embodiments, formula (1) is used in the methods, systems, etc.of the invention to diagnose a patient with melanoma. In someembodiments, formula (2) is used in the methods, systems, etc. of theinvention to diagnose a patient with melanoma. In some embodiments CCPscore is the unweighted mean of CT values for expression of the CCPgenes being analyzed, optionally normalized by the unweighted mean ofthe HK genes so that higher values indicate higher expression (in someembodiments one unit is equivalent to a two-fold change in expression).In some embodiments the CCP score ranges from −8 to 8 or from −1.6 to3.7.

In some embodiments A=0.95, B=0.61, C=0.90 (where applicable), andD=1.00 (where applicable); A=0.57 and B=0.39; or A=0.58 and B=0.41. Insome embodiments, A, B, C, and/or D is within rounding of these values(e.g., A is between 0.945 and 0.954, etc.). In some cases a formula maynot have all of the specified coefficients (and thus not incorporate thecorresponding variable(s)). For example, the embodiment mentionedimmediately previously may be applied to formula (2) so that B informula (2) is 0.61, C=0.90 (where applicable), and D=1.00 (whereapplicable). A would not be applicable as this coefficient and itscorresponding variable is not found in formula (2). In some embodimentsA is between 0.9 and 1, 0.9 and 0.99, 0.9 and 0.95, 0.85 and 0.95, 0.86and 0.94, 0.87 and 0.93, 0.88 and 0.92, 0.89 and 0.91, 0.85 and 0.9, 0.8and 0.95, 0.8 and 0.9, 0.8 and 0.85, 0.75 and 0.99, 0.75 and 0.95, 0.75and 0.9, 0.75 and 0.85, or between 0.75 and 0.8. In some embodiments Bis between 0.40 and 1, 0.45 and 0.99, 0.45 and 0.95, 0.55 and 0.8, 0.55and 0.7, 0.55 and 0.65, 0.59 and 0.63, or between 0.6 and 0.62. In someembodiments C is, where applicable, between 0.9 and 1, 0.9 and 0.99, 0.9and 0.95, 0.85 and 0.95, 0.86 and 0.94, 0.87 and 0.93, 0.88 and 0.92,0.89 and 0.91, 0.85 and 0.9, 0.8 and 0.95, 0.8 and 0.9, 0.8 and 0.85,0.75 and 0.99, 0.75 and 0.95, 0.75 and 0.9, 0.75 and 0.85, or between0.75 and 0.8. In some embodiments D is, where applicable, between 0.9and 1, 0.9 and 0.99, 0.9 and 0.95, 0.85 and 0.95, 0.86 and 0.94, 0.87and 0.93, 0.88 and 0.92, 0.89 and 0.91, 0.85 and 0.9, 0.8 and 0.95, 0.8and 0.9, 0.8 and 0.85, 0.75 and 0.99, 0.75 and 0.95, 0.75 and 0.9, 0.75and 0.85, or between 0.75 and 0.8.

In some embodiments A is between 0.1 and 0.2, 0.3, 0.4, 0.5, 0.6, 0.7,0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 0.2 and 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1,1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or20; or between 0.3 and 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3,3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between0.4 and 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.5 and 0.6, 0.7,0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 0.6 and 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5,4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.7 and0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 0.8 and 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5,5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.9 and 1, 1.5,2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 1 and 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12,13, 14, 15, or 20; or between 1.5 and 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 2 and 2.5, 3, 3.5, 4,4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 2.5 and 3,3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 3and 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 3.5 and 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 4 and 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 4.5 and 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between5 and 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 6 and 7, 8,9, 10, 11, 12, 13, 14, 15, or 20; or between 7 and 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 8 and 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 9 and 10, 11, 12, 13, 14, 15, or 20; or between 10 and 11, 12,13, 14, 15, or 20; or between 11 and 12, 13, 14, 15, or 20; or between12 and 13, 14, 15, or 20; or between 13 and 14, 15, or 20; or between 14and 15, or 20; or between 15 and 20; B is between 0.1 and 0.2, 0.3, 0.4,0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, or 20; or between 0.2 and 0.3, 0.4, 0.5, 0.6,0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, or 20; or between 0.3 and 0.4, 0.5, 0.6, 0.7, 0.8, 0.9,1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,or 20; or between 0.4 and 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3,3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between0.5 and 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, or 20; or between 0.6 and 0.7, 0.8, 0.9, 1,1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or20; or between 0.7 and 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.8 and 0.9, 1, 1.5,2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 0.9 and 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, or 20; or between 1 and 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 1.5 and 2, 2.5, 3,3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 2and 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20;or between 2.5 and 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or 20; or between 3 and 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 3.5 and 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 4 and 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or 20; or between 4.5 and 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or20; or between 5 and 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 6 and 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 7 and8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 8 and 9, 10, 11, 12, 13,14, 15, or 20; or between 9 and 10, 11, 12, 13, 14, 15, or 20; orbetween 10 and 11, 12, 13, 14, 15, or 20; or between 11 and 12, 13, 14,15, or 20; or between 12 and 13, 14, 15, or 20; or between 13 and 14,15, or 20; or between 14 and 15, or 20; or between 15 and 20; C is,where applicable, between 0.1 and 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8,0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or 20; or between 0.2 and 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5,2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 0.3 and 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4,4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.4 and0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9,10, 11, 12, 13, 14, 15, or 20; or between 0.5 and 0.6, 0.7, 0.8, 0.9, 1,1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or20; or between 0.6 and 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.7 and 0.8, 0.9,1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,or 20; or between 0.8 and 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.9 and 1, 1.5, 2, 2.5,3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between1 and 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or 20; or between 1.5 and 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, or 20; or between 2 and 2.5, 3, 3.5, 4, 4.5, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 2.5 and 3, 3.5, 4,4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 3 and 3.5,4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 3.5 and4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 4 and4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 4.5 and 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 5 and 6, 7, 8, 9,10, 11, 12, 13, 14, 15, or 20; or between 6 and 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 7 and 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 8 and 9, 10, 11, 12, 13, 14, 15, or 20; or between 9 and 10, 11,12, 13, 14, 15, or 20; or between 10 and 11, 12, 13, 14, 15, or 20; orbetween 11 and 12, 13, 14, 15, or 20; or between 12 and 13, 14, 15, or20; or between 13 and 14, 15, or 20; or between 14 and 15, or 20; orbetween 15 and 20; and D is, where applicable, between 0.1 and 0.2, 0.3,0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7,8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.2 and 0.3, 0.4, 0.5,0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10,11, 12, 13, 14, 15, or 20; or between 0.3 and 0.4, 0.5, 0.6, 0.7, 0.8,0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or 20; or between 0.4 and 0.5, 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5,3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between0.5 and 0.6, 0.7, 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8,9, 10, 11, 12, 13, 14, 15, or 20; or between 0.6 and 0.7, 0.8, 0.9, 1,1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or20; or between 0.7 and 0.8, 0.9, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6,7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 0.8 and 0.9, 1, 1.5,2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 0.9 and 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11,12, 13, 14, 15, or 20; or between 1 and 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5,6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 1.5 and 2, 2.5, 3,3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 2and 2.5, 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20;or between 2.5 and 3, 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or 20; or between 3 and 3.5, 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 3.5 and 4, 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13,14, 15, or 20; or between 4 and 4.5, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,15, or 20; or between 4.5 and 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or20; or between 5 and 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; orbetween 6 and 7, 8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 7 and8, 9, 10, 11, 12, 13, 14, 15, or 20; or between 8 and 9, 10, 11, 12, 13,14, 15, or 20; or between 9 and 10, 11, 12, 13, 14, 15, or 20; orbetween 10 and 11, 12, 13, 14, 15, or 20; or between 11 and 12, 13, 14,15, or 20; or between 12 and 13, 14, 15, or 20; or between 13 and 14,15, or 20; or between 14 and 15, or 20; or between 15 and 20. In someembodiments, A, B, and/or C is within rounding of any of these values(e.g., A is between 0.45 and 0.54, etc.).

As used herein, a patient has an “increased likelihood” of some clinicalfeature or outcome (e.g., having melanoma) if the probability of thepatient having the feature or outcome exceeds some reference probabilityor value. The reference probability may be the probability of thefeature or outcome across the general relevant patient population. Forexample, if the probability of having melanoma in the general populationis X % and a particular patient has been determined by the methods ofthe present invention to have a probability of Y % of having melanoma,and if Y>X, then the patient has an “increased likelihood” of havingmelanoma. Alternatively, as discussed above, a threshold or referencevalue may be determined and a particular patient's probability of havingmelanoma may be compared to that threshold or reference.

In some embodiments the method correlates the patient's specific score(e.g., CCP score, combined score of CCP with clinical variables) to aspecific probability (e.g., 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, 50%,55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 99%, 100%) or likelihood ofhaving melanoma.

In some embodiments, the method of diagnosis includes (1) obtaining asample from a patient suspected of having melanoma; (2) determining theexpression of a panel of genes in the sample; (3) calculating testvalues or scores; and (4) providing a report communicating the testvalue or scores. In some embodiments the report is a paper report, anauditory report, or an electronic record. In some embodiments the reportis displayed and/or stored on a computing device (e.g., handheld device,desktop computer, smart device, website, etc.). In some embodiments thereport is communicated to a physician (e.g., a report communicating thetest values or scores is provided to the physician). In some embodimentsthe report is communicated to a patient (e.g., a report communicatingthe test values or scores is provided to the patient). Providing areport can also be accomplished by transferring information (e.g., data)embodying the test values or scores to a server computer and allowing anintermediary or end-user to access such information (e.g., by viewingthe information as displayed from the server, by downloading theinformation in the form of one or more files transferred from the serverto the intermediary or end-user's device, etc.).

In other embodiments, the report may communicate scores derived fromdifferent sources and other relevant patient information. For example,the report may communicate scores derived solely from expression levels.The report may also report the scores as calculated by formula (1)formula (2), formula (3), and/or formula (4). Alternately, the reportmay communicate scores derived from a combination of expression valuescores with other components (e.g. clinical staging, personal/familyhistory, dermatopathology results, etc.) to give a potentially morecomprehensive score. In other cases, the report can communicate multiplescores (e.g. expression test value and clinical parameters, such asclinical staging) and/or a more comprehensive score. The report can alsocommunicate scores for individual genes. In some instances, the reportcan communicate scores along with control or reference values. Somereports may communicate a specific probability (e.g., 10%, 15%, 20%,25%, 30%, 35%, 40%, 45%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%,95%, 99%, 100%) or likelihood of having melanoma. Other reports maycommunicate classification of the sample as benign or malignant,predictions of melanoma risk, comparisons of melanoma risk, clinicallyactionable items, recommendations for cancer risk management and/orrecommendations for treatment. Yet other reports may include personaland family medical history.

Methods of Treating Melanoma

In one aspect, the present invention provides methods of treating apatient comprising obtaining gene expression status information for apanel of genes (e.g., obtained by the method described herein), andrecommending a treatment, prescribing a treatment, administering atreatment, creating a treatment plan, or modifying a treatment plan forthe patient based on the gene expression status. In some embodiments,the method comprises obtaining CCG expression status. In someembodiments, the method comprises obtaining immune gene expressionstatus. In some embodiments, the method comprises obtaining expressionstatus for additional genes as described herein. For example, theinvention provides a method of treating a patient comprising:

(1) determining the status of at least one CCG;

(2) determining the status of at least one immune gene;

(3) determining the status of at least one additional gene; and

(4) recommending, prescribing or administering either

-   -   (a) an active (including aggressive) treatment if the patient        has at least one of increased expression of the CCG, increased        expression of immune gene, or expression of an additional gene        that differs significantly from expression in a control sample,        or    -   (b) a passive (or less aggressive) treatment if the patient has        none of increased expression of the CCG, increased expression of        immune gene, or expression of an additional gene that differs        significantly from expression in a control sample.

In a related embodiment, the invention provides a method of treating apatient comprising:

(1) determining the status of at least one immune gene;

(2) determining the status of at least one additional gene; and

(3) recommending, prescribing or administering either

-   -   (a) an active (including aggressive) treatment if the patient        has at least one of increased expression of immune gene, or        expression of an additional gene that differs significantly from        expression in a control sample, or    -   (b) a passive (or less aggressive) treatment if the patient has        none of increased expression of immune gene, or expression of an        additional gene that differs significantly from expression in a        control sample.

In a related embodiment, the invention provides a method of treating apatient comprising:

(1) determining the status of at least one immune gene;

(2) determining the status of at least one additional gene; and

(3) creating a treatment plan comprising either

-   -   (a) more aggressive therapy components if the patient has at        least one of increased expression of immune gene, or expression        of an additional gene that differs significantly from expression        in a control sample, or    -   (b) less aggressive therapy components if the patient has none        of increased expression of immune gene, or expression of an        additional gene that differs significantly from expression in a        control sample; and

(4) implementing the treatment plan.

In some embodiments, the recommending, prescribing, or administeringsteps comprise receiving a report communicating the relevant expressionstatus (e.g., CCG status). In some embodiments, the creating a treatmentplan step comprises receiving a report communicating the relevantexpression status (e.g., CCG status). In some embodiments this reportcommunicates such status in a qualitative manner (e.g., “high” or“increased” expression). In some embodiments this report communicatessuch status indirectly by communicating a test value or score (e.g.,score reflecting likelihood of having melanoma, etc.) that incorporatessuch status.

Whether a treatment is aggressive or not will generally depend on thediagnosis or likelihood of having melanoma. For individuals diagnosedwith melanoma, or having a high likelihood of having melanoma,aggressive treatment is preferred. Those skilled in the art are familiarwith various other aggressive and less aggressive treatments for eachtype of cancer. On the other hand, if an individual has a low likelihoodof having melanoma, a less aggressive therapy could be prescribed.Therefore, for an individual having a low risk of having melanoma, amedical provider could recommend a regime of “watchful-waiting.”

A range of melanoma treatments and/or therapies are known by thoseskilled in the art. This range of melanoma therapies can vary in theiraggressiveness. In general, as the melanoma therapy increases inaggressiveness, the effectiveness of the treatment increases, but theadverse effects to the patient also increases. Skilled artisans canunderstand that the aggressiveness of the melanoma therapy that is usedto treat the patient must be balanced to take into account both theeffectiveness of the treatment and the adverse effects that will likelybe experienced by the patient. Therefore, the skilled artisan will seekto maximize the effectiveness of the treatment while minimizing theadverse effects of the treatment by selecting an appropriate level ofaggressiveness tailored to the individual patient. The appropriate levelof treatment can be selected based at least in part on the reportcommunicating the relevant expression status.

In some embodiments, a skilled artisan can incorporate the reportcommunicating the relevant expression status into the selection ofaggressiveness of treatment. A report communicating a score indicating ahigh likelihood of melanoma would indicate a more aggressive treatmentwhile a report communicating a score indicating a lower likelihood ofmelanoma would indicate a less aggressive treatment. In someembodiments, a less aggressive treatment may comprise the removal of thesuspected melanoma during a biopsy. In some embodiments, a moreaggressive treatment may include removal of the suspected melanoma aswell as removal of a small border of normal skin and a layer of tissuebeneath both the suspected melanoma and the small border of skin. Inother embodiments, a more aggressive treatment may comprise a reexcisionof the biopsy site to remove additional tissue that borders the removedbiopsy sample. In yet other embodiments, a more aggressive treatment maycomprise reexcision of additional tissue surrounding the biopsy site.

In other embodiments an even more aggressive treatment may includesurgery to remove any affected lymph nodes or a lymph node dissection(lymphadenoectomy). In other embodiments an even more aggressivetreatment may include surgery to remove any affected lymph nodes or alymph node dissection (lymphadenoectomy) followed by adjuvant therapywith interferon. In other embodiments, an even more aggressive treatmentmay include surgery to remove any affected lymph nodes as well asadditional treatments such as chemotherapy and/or radiation therapy. Inother embodiments, an even more aggressive treatment can include surgeryto remove any affected tissue or organs. In alternate embodiments, evenmore aggressive treatments can include chemotherapy. Some methods ofadministering chemotherapy include oral and intravenous treatments. Somemethods of administering chemotherapy include isolated limb perfusion ofchemotherapy drugs. In other embodiments, even more aggressivetreatments can include radiation therapy. In yet other embodiments, evenmore aggressive treatments can include biological therapy. Somebiological therapies can include interferon and/or interleukin-2. Somebiological therapies can include antibody-based therapies such asipilimumab (Yervoy). In other embodiments, even more aggressive therapycan include immunotherapy. Some immunotherapies can includeInterferon-alpha, Anti-CTLA-4, vaccines, Bacille Calmette-Guerinvaccine, Interleukin 2, and/or T-cell therapy. Some immunotherapies canalso be combined with chemotherapy and/or radiation therapy. Inalternate embodiments, even more aggressive therapies can includetargeted therapy. Some targeted therapies can include drugs such asvemurafenib (Zelboraf) used to treat advanced melanoma. Other targetedtherapies include B-RAF inhibitors and/or KIT inhibitors.

In some embodiments, the selection of the specific treatment can bebased in part on the relative expression status of the tested genes. Forexample in some embodiments, the expression profile of the individualgenes within the panel would be indicative of the selection of the typeand/or the class of therapy. A certain expression profile of theindividual genes within the panel may be indicative of melanoma that canbe effectively treated with surgery alone. On the other hand, anotherexpression profile may be indicative of melanoma that can be effectivelytreated with surgery combined with radiation therapy. In other cases,another expression profile may be indicative of melanoma that can beeffectively treated with surgery combined with chemotherapy. In yetother cases, the expression profile may be indicative of melanoma thatcan be effectively treated with only careful monitoring and regularfollow-up. In alternate embodiments, the expression profile might beindicative of melanoma that can be effectively treated with higherdosages of therapy administered at shorter intervals whereas otherexpression profiles might be indicative of lower dosages of therapyadministered at longer intervals. In some embodiments, the expressionprofile may indicate certain combinations, dosages, and/or frequenciesof therapies.

In other embodiments, a skilled artisan can utilize at least in part thereport communicating the relevant expression status to guide theselection of appropriate melanoma drugs. For example in someembodiments, the expression profile of the individual genes within thepanel may be indicative of the selection of particular melanoma drugs.In some embodiments melanoma drugs may be selected from Aldesleukin,Dabrafenib, Dacarbazine, DTIC-Dome (Dacarbazine), Intron A (RecombinantInterferon Alfa-2b), Ipilimumab, Mekinist (Trametinib), PeginterferonAlfa-2b, PEG-Intron (Peginterferon Alfa-2b), Proleukin (Aldesleukin),Recombinant Interferon Alfa-2b, Sylatron (Peginterferon Alfa-2b),Tafinlar (Dabrafenib), Trametinib, Vemurafenib, Yervoy (Ipilimumab),and/or Zelboraf (Vemurafenib). In other embodiments, melanoma drugs maybe selected from Bacille Calmette-Guerin (BCG) vaccine, interleukin-2,imiquimod, cytokines, dacarbazine (DTIC), temozolomide (Temodar),mitogen-activated protein kinase kinase (MEK) inhibitor (trametinib),and/or beta-adrenergic-blocking drugs.

In some embodiments, patients with melanoma can be treated by selectingthe relative aggressiveness of the melanoma therapy based at least inpart on the report communicating the relevant expression status (or testvalues or scores) and then administering this selected therapy. In otherembodiments, patients with melanoma can be treated by selecting therelative aggressiveness of the melanoma therapy based at least in parton the report communicating the relevant expression status,administering this selected therapy, measuring the relevant expressionstatus again, comparing the latter expression status with the previousexpression status, and continuing or modifying treatment based on thecomparison of the previous and latter expression status. In alternateembodiments, the comparison of the relevant expression status can beused to monitor the efficacy of treatment. In some cases a change in therelevant expression status from a score indicative of a higherlikelihood of melanoma to a score indicative of a lower likelihood ofmelanoma may indicate that the treatment is effective. In other cases achange in the relevant expression status from a score indicative of alower likelihood of melanoma to a score indicative of a higherlikelihood of melanoma may indicate that the treatment is lesseffective. Where a change in the expression status indicates aneffective treatment the treatment may be continued or modified tocomprise a less aggressive treatment. Where a change in the expressionstatus indicates a less effective treatment the treatment may becontinued or modified to comprise a more aggressive treatment.

In yet other embodiments, the skilled artisan can create or modify atreatment plan for the individual patient based at least in part on thereport communicating the relevant expression status. In someembodiments, the selection of different therapy components that comprisethe treatment plan can be based at least in part on the reportcommunicating the relevant expression status (or test values or scores).For example, in some instances, the report will indicate a lowlikelihood of melanoma and the treatment plan may comprise lessaggressive therapy components. The less aggressive therapy componentscan include removal of the suspected melanoma and follow up monitoringof the patient. In other cases, the report may indicate a highlikelihood of melanoma and the treatment plan may comprise moreaggressive therapy components. Components of a more aggressive treatmentplan can include removal of the suspected melanoma and surroundingtissue, reexcision of the biopsy site to remove additional surroundingtissue, chemotherapy, radiation therapy and/or biological therapy. Inalternate embodiments, the report communicating the relevant expressionstatus can be used in part to select the different elements within eachcomponent of the treatment plan. As a non-limiting example, the reportcan be used to select individual melanoma drugs that comprise thechemotherapy component of the treatment plan. In other non-limitingexamples, the report can be used to select the types of radiation, theamounts of radiation, and/or the dosing regime of the radiationcomponent of the treatment plan. In some embodiments, the treatment plancan further comprise continued measurement of the relevant expressionstatus to determine the efficacy of the treatment plan. In otherembodiments, this continued measurement of the efficacy of treatment canbe used to modify the treatment plan.

Systems for Diagnosing and Treating Melanoma

The results of any analyses according to the invention will often becommunicated to physicians, genetic counselors and/or patients (or otherinterested parties such as researchers) in a transmittable form that canbe communicated or transmitted to any of the above parties. Such a formcan vary and can be tangible or intangible. The results can be embodiedin descriptive statements, diagrams, photographs, charts, images or anyother visual forms. For example, graphs showing expression or activitylevel or sequence variation information for various genes can be used inexplaining the results. Diagrams showing such information for additionaltarget gene(s) are also useful in indicating some testing results. Thestatements and visual forms can be recorded on a tangible medium such aspapers, computer readable media such as floppy disks, compact disks,etc., or on an intangible medium, e.g., an electronic medium in the formof email or website on internet or intranet. In addition, results canalso be recorded in a sound form and transmitted through any suitablemedium, e.g., analog or digital cable lines, fiber optic cables, etc.,via telephone, facsimile, wireless mobile phone, internet phone and thelike.

Thus, the information and data on a test result can be produced anywherein the world and transmitted to a different location. As an illustrativeexample, when an expression level, activity level, or sequencing (orgenotyping) assay is conducted outside the United States, theinformation and data on a test result may be generated, cast in atransmittable form as described above, and then imported into the UnitedStates. Accordingly, the present invention also encompasses a method forproducing a transmittable form of information on at least one of (a)expression level or (b) activity level for at least one patient sample.The method comprises the steps of (1) determining at least one of (a) or(b) above according to methods of the present invention; and (2)embodying the result of the determining step in a transmittable form.The transmittable form is the product of such a method.

Techniques for analyzing such expression, activity, and/or sequence data(indeed any data obtained according to the invention) will often beimplemented using hardware, software or a combination thereof in one ormore computer systems or other processing systems capable ofeffectuating such analysis.

Thus, the present invention further provides a system for determininggene expression in a tumor sample, comprising: (1) a sample analyzer fordetermining the expression levels of a panel of genes in a patientsample, wherein the sample analyzer contains the patient sample, or cDNAmolecules from mRNA expressed of the panel of genes derived from thesample; (2) a first computer program for (a) receiving gene expressiondata from the panel of genes, (b) weighting the determined expression ofeach of the test genes, and (c) combining the weighted expression toprovide a test value; and optionally (3) a second computer program forcomparing the test value to one or more reference values each associatedwith a predetermined degree of risk of melanoma.

In another embodiment, the amount of RNA transcribed from the panel ofgenes including test genes is measured in the sample. In addition, theamount of RNA of one or more housekeeping genes in the sample is alsomeasured, and used to normalize or calibrate the expression of the testgenes, as described above.

The sample analyzer can be any instruments useful in determining geneexpression, including, e.g., a sequencing machine, a real-time PCRmachine, and a microarray instrument.

The computer-based analysis function can be implemented in any suitablelanguage and/or browsers. For example, it may be implemented with Clanguage and preferably using object-oriented high-level programminglanguages such as Visual Basic, SmallTalk, C++, and the like. Theapplication can be written to suit environments such as the MicrosoftWindows™ environment including Windows™ 98, Windows™ 2000, Windows™ NT,and the like. In addition, the application can also be written for theMacIntosh™, SUN™, UNIX or LINUX environment. In addition, the functionalsteps can also be implemented using a universal or platform-independentprogramming language. Examples of such multi-platform programminglanguages include, but are not limited to, hypertext markup language(HTML), JAVA™, JavaScript™, Flash programming language, common gatewayinterface/structured query language (CGI/SQL), practical extractionreport language (PERL), AppleScript™ and other system script languages,programming language/structured query language (PL/SQL), and the like.Java™- or JavaScript™-enabled browsers such as HotJava™, Microsoft™Explorer™, or Netscape™ can be used. When active content web pages areused, they may include Java™ applets or ActiveX™ controls or otheractive content technologies.

The analysis function can also be embodied in computer program productsand used in the systems described above or other computer- orinternet-based systems. Accordingly, another aspect of the presentinvention relates to a computer program product comprising acomputer-usable medium having computer-readable program codes orinstructions embodied thereon for enabling a processor to carry out genestatus analysis. These computer program instructions may be loaded ontoa computer or other programmable apparatus to produce a machine, suchthat the instructions which execute on the computer or otherprogrammable apparatus create means for implementing the functions orsteps described above. These computer program instructions may also bestored in a computer-readable memory or medium that can direct acomputer or other programmable apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory or medium produce an article of manufacture includinginstructions which implement the analysis. The computer programinstructions may also be loaded onto a computer or other programmableapparatus to cause a series of operational steps to be performed on thecomputer or other programmable apparatus to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide steps for implementingthe functions or steps described above.

Thus one aspect of the present invention provides a system fordetermining whether a patient has increased likelihood of havingmelanoma. Generally speaking, the system comprises (1) computer programfor receiving, storing, and/or retrieving a patient's gene status data(e.g., expression level, activity level, variants) and optionallyclinical parameter data (e.g., clinical staging); (2) computer programfor querying this patient data; (3) computer program for concludingwhether there is an increased likelihood of having melanoma based onthis patient data; and optionally (4) computer program foroutputting/displaying this conclusion. In some embodiments this computerprogram for outputting the conclusion may comprise a computer programfor informing a health care professional of the conclusion

The practice of the present invention may also employ conventionalbiology methods, software and systems. Computer software products of theinvention typically include computer readable media havingcomputer-executable Instructions for performing the logic steps of themethod of the invention. Suitable computer readable medium includefloppy disk, CD-ROM/DVD/DVD-ROM, hard-disk drive, flash memory, ROM/RAM,magnetic tapes and etc. Basic computational biology methods aredescribed in, for example, Setubal et al., Introduction to ComputationalBiology Methods (PWS Publishing Company, Boston, 1997); Salzberg et al.(Ed.), Computational Methods in Molecular Biology, (Elsevier, Amsterdam,1998); Rashidi & Buehler, Bioinformatics Basics: Application inBiological Science and Medicine (CRC Press, London, 2000); and Ouelette& Bzevanis, Bioinformatics: A Practical Guide for Analysis of Gene andProteins (Wiley & Sons, Inc., 2nd ed., 2001); see also, U.S. Pat. No.6,420,108.

The present invention may also make use of various computer programproducts and software for a variety of purposes, such as probe design,management of data, analysis, and instrument operation. See U.S. Pat.Nos. 5,593,839; 5,795,716; 5,733,729; 5,974,164; 6,066,454; 6,090,555;6,185,561; 6,188,783; 6,223,127; 6,229,911 and 6,308,170. Additionally,the present invention may have embodiments that include methods forproviding genetic information over networks such as the Internet asshown in U.S. Ser. No. 10/197,621 (U.S. Pub. No. 20030097222); Ser. No.10/063,559 (U.S. Pub. No. 20020183936), Ser. No. 10/065,856 (U.S. Pub.No. 20030100995); Ser. No. 10/065,868 (U.S. Pub. No. 20030120432); Ser.No. 10/423,403 (U.S. Pub. No. 20040049354).

Techniques for analyzing such expression, activity, and/or sequence data(indeed any data obtained according to the invention) will often beimplemented using hardware, software or a combination thereof in one ormore computer systems or other processing systems capable ofeffectuating such analysis.

Thus one aspect of the present invention provides systems related to theabove methods of the invention. In one embodiment the invention providesa system for determining gene expression in a sample, comprising:

-   -   (1) a sample analyzer for determining the expression levels in a        sample of a panel of genes, wherein the sample analyzer contains        the sample, RNA from the sample and expressed from the panel of        genes, or DNA synthesized from said RNA;    -   (2) a first computer program for        -   (a) receiving gene expression data on one or more test genes            selected from the panel of genes,        -   (b) weighting the determined expression of each of the one            or more test genes with a predefined coefficient, and        -   (c) combining the weighted expression to provide a test            value; and optionally    -   (3) a second computer program for comparing the test value to        one or more reference values each associated with a        predetermined degree of risk of having melanoma.

In another embodiment the invention provides a system for determininggene expression in a sample, comprising: (1) a sample analyzer fordetermining the expression levels of a panel of genes in a sample,wherein the sample analyzer contains the sample which is a nevus or molesuspected of having melanoma, RNA from the sample and expressed from thepanel of genes, or DNA synthesized from said RNA; (2) a first computerprogram for (a) receiving gene expression data on one or more test genesselected from the panel of genes, (b) weighting the determinedexpression of each of the test genes with a predefined coefficient, and(c) combining the weighted expression to provide a test value, whereinthe test genes comprise immune genes and additional genes; andoptionally (3) a second computer program for comparing the test value toone or more reference values each associated with a predetermined degreeof risk of having melanoma. In some embodiments, the system furthercomprises a display module displaying the comparison between the testvalue and the one or more reference values, or displaying a result ofthe comparing step, or displaying the patient's diagnosis and/or degreeof risk of having melanoma.

In a preferred embodiment, the amount of RNA transcribed from the panelof genes including test genes (and/or DNA reverse transcribed therefrom)is measured in the sample. In addition, the amount of RNA of one or morehousekeeping genes in the sample (and/or DNA reverse transcribedtherefrom) is also measured, and used to normalize or calibrate theexpression of the test genes, as described above.

The sample analyzer can be any instrument useful in determining geneexpression, including, e.g., a sequencing machine (e.g., IlluminaHiSeg™, Ion Torrent PGM, ABI SOLiD™ sequencer, PacBio RS, HelicosHeliscope™, etc.), a real-time PCR machine (e.g., ABI 7900, FluidigmBioMark™, etc.), a microarray instrument, etc.

FIG. 1 illustrates a system 100 for performing computer-assisted methodsof diagnosing, detecting, screening and/or treating melanoma in apatient.

System 100 comprises a patient/medical provider interface module 10comprising a medical provider 11 and a patient 12. The medical provider11 comprises a doctor and/or other medical staff that care for patient12. The medical provider collects a complete medical history frompatient 12 including but not limited to symptoms, past medical history,and/or family history. The medical provider 11 also conducts a physicalexamination of the patient 12 and obtains a sample of the patient 12.

System 100 further comprises a data processing device 20 comprising asample analyzer module 21. The sample of the patient is conveyed frompatient/medical provider interface module 10 to the sample analyzerdevice 20. The sample analyzer module 21 determines the gene expressionlevels of a panel of biomarkers in the patient sample. The panel ofbiomarkers may comprise biomarkers useful for determining the presenceof melanoma in the patient 12. The panel of biomarkers may furthercomprise housekeeper genes useful for normalizing the levels ofbiomarker panel. The sample analyzer module 21 can comprise anyinstrument useful in determining gene expression levels including, e.g.a sequencing machine (e.g., Illumina HiSeg™, Ion Torrent PGM, ABI SOLiD™sequencer, PacBio RS, Helicos Heliscope™, etc.), a real-time PCR machine(e.g., ABI 7900, Fluidigm BioMark™, etc.), a microarray instrument, etc.

System 100 further comprises a data processing device 30 comprising amedical history database module 31. The medical history database module31 may comprise a complete medical history from patient 12 includingfamily history information comprising the number of family members of apatient diagnosed with cancer, including melanoma. The family historyinformation may also comprise the degree of relationship to a patient ofeach family member diagnosed with cancer, including melanoma. Themedical history database module 31 may be in communication withpatient/medical provider interface module 10. The medical historydatabase module 31 may be configured to receive the patient's medicalhistory from patient/medical provider interface module 10 either as aphysical record or as an electronic transmission.

The system 100 further comprises a data processing device 40 comprisinga patient information database module 41. The patient informationdatabase module 41 comprises patient information comprising geneexpression levels of a panel of biomarkers of the patient 12. Thepatient information database module 41 may be in communication withsample analyzer module 41. The patient information database module 41may be configured to receive the patient's gene expression levels fromthe sample analyzer module 21 either as a physical record or as anelectronic transmission.

System 100 further comprises a data processing device 50 and a dataprocessing device 60. Data processing device 50 comprises a scoringmodule 51. Data processing device 60 comprises a biomarker informationdatabase module 61. The biomarker information database module 61comprises biomarker information comprising threshold level informationfor each biomarker of a panel of biomarkers, wherein the panel ofbiomarkers comprises positive biomarkers, negative biomarkers, or both,wherein a level statistically significantly above a threshold level foreach particular positive biomarker is indicative of melanoma in apatient and a level statistically significantly below a threshold levelfor each particular negative biomarker is indicative of the presence ofmelanoma in a patient.

The scoring module 51 may be in communication with the biomarkerinformation database module 61 and the patient information databasemodule 41. The scoring module 51 may be configured to compare biomarkerinformation and patient information to generate a score representing thecomparison between the biomarker information and the patientinformation. The scoring module 51 may be further configured tonormalize, average, and apply weighting of sub-groups of biomarkersduring the generation of the score. The scoring module 51 may be furtherconfigured to algebraically add and/or subtract subgroups of biomarkersduring the generation of the score.

The data processing device 50 may further comprise an evaluation module52 in communication with the scoring module 51. The evaluation module 22may further be in communication with the biomarker information databasemodule 61. The evaluation module 52 may further be in communication themedical history database module 31. The evaluation module 52 may beconfigured to determine a probability of the presence of melanoma in thepatient based on the patient score as compared to scores of groups ofpatients diagnosed with melanoma and scores of groups of patients thatwere not diagnosed with melanoma. The evaluation module 52 may befurther configured to determine a probability of the presence ofmelanoma in the patient based on the patient score and the patientinformation. The data processing device 50 may further comprise adiagnostic module 53 in communication with the evaluation module 52. Thediagnostic module 53 may be configured to determine additional suggesteddiagnostic procedures based on a patient's probability of melanoma. Thedata processing device 50 may further comprise a report generationmodule 54. The report generation module 54 can comprise any device thataggregates the data and process of data processing device 50 into areport. The report produced by report generation module 54 can comprisethe score. The report can further comprise the probability of thepresence of melanoma. The report can further comprise additionalsuggested diagnostic procedures. The report can further comprisesuggested treatments.

System 100 further comprises a data processing device 70. Dataprocessing device 70 comprises communication means 71. Communicationmeans 71 is in communication with report generation module 54 andpatient/medical provider interface module 10. Communication means 71 isconfigured to transmit the report generated by report generation module54 to the patient/medical provider interface module 10. The report canfurther be transmitted by electronic means to the patient/medicalprovider interface module 10. Communication means 71 can also transmitthe report by printing on a tangible medium such as paper and conveyingthe report to patient/medical provider interface module 10. Uponreceiving the transmitted report, the medical provider 11 can treat thepatient 12 according to the information in the report. The medicalprovider 11 can further diagnose the patient 12 based on the report. Themedical provider 11 can further create or modify a treatment plan forthe patient 12 based on the report. The medical provider 11 can furtherfollow the suggested additional diagnostic test from the report and/orfollow the suggested treatments in the report.

Accordingly, the various components, modules, systems, and/or featuresdisclosed herein may be embodied as modules within a system. Such asystem may be implemented in software, firmware, hardware, and/orphysical infrastructure. Although not always explicitly named herein, amodule may be identified (named) based on a function it performs. Forexample, a module that is configured to calculate something may comprisespecific hardware, software, or firmware and be properly referred to asa “calculation module.”

Embodiments may also be provided as a computer program product includinga non-transitory machine-readable medium having stored thereoninstructions that may be used to program, or be executed on, a computer(or other electronic device) to perform processes described herein. Themachine-readable medium may include, but is not limited to, hard drives,floppy diskettes, optical disks, CD-ROMs, DVD-ROMs, ROMs, RAMs, EPROMs,EEPROMs, magnetic or optical cards, solid-state memory devices, or othertypes of media/machine-readable media suitable for storing electronicinstructions. Moreover, a computer program product may be run, executed,downloaded, and/or otherwise used locally or remotely via a network.

It should be understood that references to “a data processing device”may refer to the same device or one or more different devices. Forexample, certain steps of the computer-assisted methods may be performedon a device controlled by a diagnostic service provider and other stepsmay be performed on a device controlled by a medical practitioner.Likewise, the data processing devices 10, 20, 30, 40, 50, 60, and 70 maybe a single device or, for example, the data processing device 50 may bemultiple data processing devices.

In certain embodiments, the computer-implemented method may beconfigured to identify a patient as having or not having pancreaticcancer. For example, the computer-implemented method may be configuredto inform a physician that a particular patient has pancreatic cancer.Alternatively or additionally, the computer-implemented method may beconfigured to actually suggest a particular course of treatment based onthe answers to/results for various queries.

Probes and Kits

In some embodiments the invention provides a probe comprising anisolated oligonucleotide capable of selectively hybridizing to at leastone of the genes in Table 1, 3 or Panels A through I. The terms “probe”and “oligonucleotide” (also “oligo”), when used in the context ofnucleic acids, interchangeably refer to a relatively short nucleic acidfragment or sequence. The invention also provides primers useful in themethods of the invention. “Primers” are probes capable, under the rightconditions and with the right companion reagents, of selectivelyamplifying a target nucleic acid (e.g., a target gene). In the contextof nucleic acids, “probe” is used herein to encompass “primer” sinceprimers can generally also serve as probes.

The probe can generally be of any suitable size/length. In someembodiments the probe has a length from about 8 to 200, 15 to 150, 15 to100, 15 to 75, 15 to 60, or 20 to 55 bases in length. They can belabeled with detectable markers with any suitable detection markerincluding but not limited to, radioactive isotopes, fluorophores,biotin, enzymes (e.g., alkaline phosphatase), enzyme substrates, ligandsand antibodies, etc. See Jablonski et al., Nucleic Acids Res. (1986)14:6115-6128; Nguyen et al., Biotechniques (1992) 13:116-123; Rigby etal., J. Mol. Biol. (1977) 113:237-251. Indeed, probes may be modified inany conventional manner for various molecular biological applications.Techniques for producing and using such oligonucleotide probes areconventional in the art.

Probes according to the invention can be used in thehybridization/amplification/detection techniques discussed above. Thus,some embodiments of the invention comprise probe sets suitable for usein a microarray. In some embodiments the probe sets have a certainproportion of their probes directed to CCGs—e.g., a probe set consistingof 10%, 20%, 30%, 40%, 50%, 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%,96%, 97%, 98%, 99%, or 100% probes specific for CCGs. In someembodiments the probe set comprises probes directed to at least 1, 2, 3,4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 40, 45, 50, 60, 70,80, 90, 100, 125, 150, or more, or all, of the genes in Table 1, 3 orPanels A through I, or any of the panels disclosed in Tables WW-ZZ. Suchprobe sets can be incorporated into high-density arrays comprising5,000, 10,000, 20,000, 50,000, 100,000, 200,000, 300,000, 400,000,500,000, 600,000, 700,000, 800,000, 900,000, or 1,000,000 or moredifferent probes.

In another aspect of the present invention, a kit is provided forpracticing the diagnosis of the present invention. The kit may include acarrier for the various components of the kit. The carrier can be acontainer or support, in the form of, e.g., bag, box, tube, rack, and isoptionally compartmentalized. The carrier may define an enclosedconfinement for safety purposes during shipment and storage. The kitincludes various components useful in determining the status of one ormore panels of genes as described herein, and one or more housekeepinggene markers, using the above-discussed detection techniques. Forexample, the kit many include oligonucleotides specifically hybridizingunder high stringency to mRNA or cDNA of the genes in Table 1, 3 orPanels A through I, or any of the panels disclosed in Tables WW-ZZ. Sucholigonucleotides can be used as PCR primers in RT-PCR reactions, orhybridization probes. In some embodiments the kit comprises reagents(e.g., probes, primers, and or antibodies) for determining theexpression level of a panel of genes, where said panel comprises atleast 25%, 30%, 40%, 50%, 60%, 75%, 80%, 90%, 95%, 99%, or 100% CCGs. Insome embodiments the kit consists of reagents (e.g., probes, primers,and or antibodies) for determining the expression level of no more than2500 genes, wherein at least 5, 10, 15, 20, 30, 40, 50, 60, 70, 80, 90,100, 120, 150, 200, 250, or more of these genes are CCGs.

The oligonucleotides in the detection kit can be labeled with anysuitable detection marker including but not limited to, radioactiveisotopes, fluorophores, biotin, enzymes (e.g., alkaline phosphatase),enzyme substrates, ligands and antibodies, etc. See Jablonski et al.,Nucleic Acids Res., 14:6115-6128 (1986); Nguyen et al., Biotechniques,13:116-123 (1992); Rigby et al., J. Mol. Biol., 113:237-251 (1977).Alternatively, the oligonucleotides included in the kit are not labeled,and instead, one or more markers are provided in the kit so that usersmay label the oligonucleotides at the time of use.

In another embodiment of the invention, the detection kit contains oneor more antibodies selectively immunoreactive with one or more proteinsencoded by any of the genes in Table 1, 3 or Panels A through I, or anyof the panels disclosed in Tables WW-ZZ.

Various other components useful in the detection techniques may also beincluded in the detection kit of this invention. Examples of suchcomponents include, but are not limited to, Taq polymerase,deoxyribonucleotides, dideoxyribonucleotides, other primers suitable forthe amplification of a target DNA sequence, RNase A, and the like. Inaddition, the detection kit preferably includes instructions on usingthe kit for practice the diagnosis method of the present invention usinghuman samples.

EXAMPLES Example 1

In this example we determined whether cell cycle progression genes candifferentiate between malignant melanoma and non-malignant nevi.Specifically, this example assesses whether melanoma can bedifferentiated from benign nevi, as well as dysplastic nevi.

Materials and Methods

Samples: 31 FFPE skin samples, with 3×10 mm slides for each sample, anda 4 mm slide for H&E review between the ages of 10-11 years old wereobtained from an academic institution. The samples consisted of 11benign nevi, 10 dysplastic nevi and 10 melanoma samples. Table 4 listssamples and corresponding clinical details.

TABLE 4 Skin samples with diagnosis, clinical data, and CCP score. SlideHK CCP Biopsy BLD ID Diagnosis CCP Mean STdv Quality date Sex AgeLocation 01095572- MG12- Compound 0.61 19.81 0.04 Very Jan. 3, 2001 F 21Chest BLD 23 nevus Good 01095574- MG12- Compound 0.79 19370 0.02 VeryJan. 5, 2001 F 31 Neck BLD 29 nevus Good 01095579- MG12- Compound 1.6023.02 0.08 Very Jan. 16, 2001 M 17 Jaw BLD 27 nevus Good 01095624- MG12-Compound 0.74 20.22 0.13 Very Jan. 8, 2001 F 57 Ear BLD 30 nevus Good01095553- MG12- Intradermal 0.67 17.41 0.02 Very Jan. 5, 2001 M 56 NeckBLD 24 nevus Good 01095559- MG12- Intradermal 0.39 19.48 0.04 Very Feb.2, 2001 M 43 Back BLD 33 nevus Good 01095560- MG12- Intradermal −0.1818.04 0.04 Very Jan. 16, 2001 F 31 Jaw BLD 25 nevus Good 01095569- MG12-Intradermal 0.27 19.75 0.08 Very Jan. 5, 2001 M 30 Shoulder BLD 32 nevusGood 01095571- MG12- Intradermal 1.05 19.66 0.02 Very Jan. 2, 2001 M 36Cheek BLD 28 nevus Good 01095576- MG12- Intradermal 0.42 18.34 0.12 VeryJan. 16, 2001 M 17 Chin BLD 26 nevus Good 01095580- MG12- Intradermal0.74 20.36 0.05 Very Jan. 18, 2001 F 53 Neck BLD 31 nevus Good 01095556-MG12- Compound 1.03 20.23 0.04 Very Jan. 29, 2001 F 26 Chest BLD 35dysplastic Good nevus 01095570- MG12- Compound 0.23 19.56 0.10 Very Feb.1, 2001 M 52 Thigh BLD 42 dysplastic Good nevus 01095577- MG12- Compound0.93 19.21 0.02 Very Mar. 19, 2001 M 41 Back BLD 40 dysplastic Goodnevus 01095620- MG12- Compound 1.33 20.48 0.02 Very Feb. 22, 2001 F 30Back BLD 39 dysplastic Good nevus 01095622- MG12- Compound 0.54 17.070.02 Very Jan. 25, 2001 M 35 Back BLD 34 dysplastic Good nevus 01095626-MG12- Compound 0.67 24.95 0.26 Good Feb. 7, 2001 F 32 Abdomen BLD 36dysplastic nevus 01095552- MG12- Junctional 0.91 20.74 0.16 Very Mar. 5,2001 M 27 Neck BLD 41 dysplastic Good nevus 01095554- MG12- Junctional0.70 21.11 0.02 Very Feb. 21, 2001 M 45 Chest BLD 38 dysplastic Goodnevus 01095562- MG12- Junctional 1.45 21.86 0.04 Very Feb. 14, 2001 F 40Back BLD 37 dysplastic Good nevus 01095578- MG12- Junctional 1.28 20.470.05 Very Jan. 17, 2001 F 69 Thigh BLD 43 dysplastic Good nevus01095621- MG12- Nodular NA 30.34 NA Rejected Nov. 13, 2001 F 9 Back BLD46 melanoma 01095551- MG12- SSM 1.50 21.13 0.01 Very Sep. 26, 2001 M 51Arm BLD 50 Good 01095555- MG12- SSM 1.28 19.73 0.07 Very Jul. 1, 2002 M61 Back BLD 53 Good 01095557- MG12- SSM 2.20 21.07 0.06 Very Jun. 24,2001 F 36 Chest BLD 52 Good 01095558- MG12- SSM 1.67 19.19 0.03 VeryJan. 4, 2001 F 68 Arm BLD 44 Good 01095561- MG12- SSM 1.76 20.20 0.03Very Mar. 1, 2001 M 36 Back BLD 51 Good 01095573- MG12- SSM 1.67 20.020.05 Very Mar. 14, 2001 F 36 Knee BLD 45 Good 01095575- MG12- SSM 2.3919.54 0.04 Very Aug. 16, 2001 F 38 Back BLD 49 Good 01095623- MG12- SSM2.12 19.82 0.02 Very Jul. 21, 2001 M 38 Shoulder BLD 48 Good 01095625-MG12- SSM 2.01 20.46 0.05 Very Aug. 7, 2001 M 43 shoulder BLD 47 GoodSSM = superficial spreading melanoma

Sample Processing, CCP score generation, and analysis: 4 mm slides werestained to make H&E slides and reviewed by a pathologist who circled thelesion (either the nevus or the melanoma). Using the H&E slide, thelesions were dissected and removed from each of the three 10 μM slides.All three dissected lesions from a single patient were pooled.

RNA was extracted from samples and RNA expression levels were determinedusing standard qPCR techniques. Of the 31 samples, 30 were successfullyrun and generated a CCP score (see Table 4 for data).

CCP scores of the melanoma samples were compared to the other twogroups, benign nevi and dysplastic nevi, as well as to dysplastic nevialone, using the Student's t-test, to determine if the CCP scores of thegroups were different in a statistically significant manner.

Results

We observed the melanoma samples had different CCP scores than both nevisubgroups combined, in a very statistically significant manner (p-value,1.4×10−6, see FIG. 2 ). When using this data in a diagnostic model, themelanoma samples could be identified with an AUC of 0.97. On average,melanoma CCP scores were 1.08 higher than nevi CCP scores. See Table 4for a list of all data. FIG. 2 shows the distribution of the CCP scoresfrom all 30 samples with a score, separated by the clinical diagnosis.

We also observed the melanoma samples had different CCP scores comparedto just dysplastic nevi, in a statistically significant manner (p-value,3.9×10−5, see FIG. 2 ). When using this data in a diagnostic model, themelanoma samples could be identified, compared to just the dysplasticnevi, with an AUC of 0.97. On average, melanoma CCP scores were 0.94higher than nevi CCP scores.

Finally, we observed the benign nevi were not statistically differentthan the dysplastic nevi (p-value, 0.17), indicating that when nevibecome dysplastic they are not replicating at a faster rate, and onlyupon transitioning to a melanoma do the cells begin to replicate faster.The data indicates that the melanoma samples are replicating at a 2-foldhigher rate than nevi.

Discussion

These data show the measurement of CCP scores can differentiate betweenmalignant melanoma and nonmalignant nevi. More specifically these datashow that a CCP score can differentiate between melanoma and dysplasticnevi. While the average difference between the two groups is moderate(˜1 CCP unit), precision of the measurements allows for good separationof the two datasets.

Example 2

In this example we determined whether certain CCP genes differentiatednevi and melanoma more effectively (Table 6). We indeed observed thatthere were certain CCP genes that had much better AUC values thanothers, even though all but one gene had an AUC >0.8, which was stillquite impressive. We decided to move forward and selected ten CCP geneswhose AUC scores were >0.95 (see Table 6). Ten CCP genes were sufficientto produce a robust and reliable CCP score.

TABLE 6 Correlation Continue with overall to CCPGene Assay AUC CCP Scorenext stage SKA1 Hs00536843_m1 1.00 0.88 Yes DTL Hs00978565_m1 1.00 0.87Yes CEP55 Hs00216688_m1 0.99 0.81 Yes FOXM1 Hs01073586_m1 0.98 0.94 YesPLK1 Hs00153444_m1 0.98 0.87 Yes PBK Hs00218544_m1 0.97 0.96 Yes CENPFHs00193201_m1 0.97 0.85 Yes DLGAP5 Hs00207323_m1 0.96 0.95 Yes MCM10Hs00960349_m1 0.96 0.84 Yes RRM2 Hs00357247_g1 0.96 0.96 Yes ORC6LHs00204876_m1 0.95 0.88 BIRC5 Hs00153353_m1 0.95 0.82 NUSAP1Hs01006195_m1 0.95 0.95 ASF1B Hs00216780_m1 0.93 0.89 RAD54LHs00269177_m1 0.93 0.84 CDKN3 Hs00193192_m1 0.93 0.92 ASPM Hs00411505_m10.93 0.95 BUB1B Hs01084828_m1 0.92 0.94 TKI Hs01062125_m1 0.92 0.93KIF20A Hs00993573_m1 0.91 0.81 CDK1 Hs00364293_m1 0.91 0.91 CDC20Hs03004916_g1 0.91 0.92 RAD51 Hs001534L8_m1 0.91 0.91 CDCA8Hs0098365S_m1 0.91 0.89 KIF11 Hs00189698_m1 0.90 0.65 PTTG1Hs00851754_u1 0.90 0.90 PRC1 Hs00187740_m1 0.89 0.92 TOP2A Hs00172214_m10.86 0.77 KIAA0101 Hs00207134_m1 0.86 0.89 CENPM Hs00608780_m1 0.81 0.63CDCA3 Hs00229905_m1 0.71 0.65 AUC indicates differentiation betweenmelanoma and all nevi.

Example 3

This example assesses a variety of potential biomarkers to determine iftheir altered expression can differentiate between malignant melanomaand non-malignant nevi.

Materials and Methods

Samples: Biomarker discovery was performed using two independentdatasets. The first dataset consisted of 31 samples (Group 1). See Table7 for a list of these samples and their clinical details. The seconddataset consisted of 53 samples (Group 2). See Table R for a list ofthese samples and their clinical details.

TABLE 7 Group 1 samples. Sample ID Diagnosis Subtype Biopsy date Age SexLocation MG12-50 Melanoma Superficial Spreading Sep. 26, 2001 51 M ArmMG12-41 Nevus Dysplastic low/Junctional Mar. 5, 2001 27 M Neck MG12-24Nevus Intradermal Jan. 5, 2001 56 M Neck MG12-38 Nevus Dysplasticlow/Junctional Feb. 21, 2001 45 M Chest MG12-53 Melanoma SuperficialSpreading Jul. 1, 2002 61 M Chest MG12-35 Nevus Dysplastic low/CompoundJan. 29, 2001 26 F Chest MG12-52 Melanoma Superficial Spreading Jun. 24,2002 36 F Chest MG12-44 Melanoma Superficial Spreading Jan. 4, 2001 68 Farm MG12-33 Nevus Intradermal Feb. 2, 2001 43 M back MG12-25 NevusIntradermal Jan. 16, 2001 31 F jaw MG12-51 Melanoma SuperficialSpreading Mar. 1, 2002 36 M Back MG12-37 Nevus Dysplastic low/JunctionalFeb. 14, 2001 40 F Back MG12-32 Nevus Intradermal Jan. 5, 2001 30 Mshoulder MG12-42 Nevus Dysplastic low/Compound Feb. 1, 2001 52 M ThighMG12-28 Nevus Intradermal Jan. 2, 2001 36 M Cheek MG12-23 Nevus CompoundJan. 3, 2001 21 F Chest MG12-45 Melanoma Superficial Spreading Mar. 14,2001 36 F Knee MG12-29 Nevus Compound Jan. 5, 2001 31 F Neck MG12-49Melanoma Superficial Spreading Aug. 16, 2001 38 F Back MG12-26 NevusIntradermal Jan. 6, 2001 17 M Chin MG12-40 Nevus Dysplastic low/CompoundMar. 19, 2001 41 M Back MG12-43 Nevus Dysplastic low/Junctional Jan. 17,2001 69 F Thigh MG12-27 Nevus Compound Jan. 16, 2001 17 M Jaw MG12-31Nevus Intradermal Jan. 18, 2001 53 F Neck MG12-39 Nevus Dysplasticlow/Compound Feb. 22, 2001 30 F Back MG12-46 Melanoma Nodular Nov. 13,2001 9 F Back MG12-34 Nevus Dysplastic low/Compound Jan. 25, 2001 35 Mback MG12-48 Melanoma Superficial Spreading Jul. 21, 2001 38 M ShoulderMG12-30 Lost sample MG12-47 Melanoma Superficial Spreading Aug. 7, 200143 M Shoulder MG12-36 Nevus Dysplastic low/Compound Feb. 7, 2001 32 Fabdomen

TABLE 8 Group 2 Samples qPCR ID Sample ID Diagnosis Subtype Biopsy DateAge Sex Location 1 P09-3235 Nevus Intradermal Nov. 25, 2009 44 F Torso 2P08-1961 Nevus Compound Feb. 8, 2008 65 M Torso 3 P05-363 MelanomaNodular Jan. 11, 2005 38 M Leg 4 P10-2736 F1 Nevus Intradermal Jan. 27,2010 34 M Head 5 CPP-11-33494 Melanoma Nodular Nov. 1, 2011 69 M Head 6CPP-09-27303 Melanoma Superficial Oct. 1, 2009 47 F Torso Spreading 7CPP-10-2397 Nevus Blue Jan. 26, 2010 51 M Head 8 P09-35648 NevusDysplastic low Dec. 18, 2009 44 M Leg 9 CPP-11-07506 Nevus Dysplasticlow Mar. 1, 2011 40 F Torso 10 P10-1574 A1 Nevus Intradermal Jan. 17,2010 335 F Torso 11 C07-896 E4 Melanoma Nodular Jan. 18, 2007 59 M Head12 P10-494 A1 Nevus Intradermal Jan. 6, 2010 53 M Head 13 CPP-09-36545A9 Melanoma Nodular 60 M Torso 14 P10-3676 A11 Melanoma Superficial Feb.6, 2010 79 M Leg Spreading 15 C06-10614 Melanoma Superficial Jan. 11,2006 34 M Head Spreading 16 CPP-10-10782 B1 Nevus Dysplastic low Apr. 1,2010 30 M Torso 17 CPP-10-3651 Nevus Blue Feb. 5, 2010 39 F Head 18CPP-10-10782 A1 Nevus Dysplastic low Apr. 1, 2010 30 M Torso 19CPP-10-04915 Melanoma Acral Feb. 1, 2010 71 M Leg 20 CPP-09-8204Melanoma Nodular Apr. 3, 2009 60 M Head 21 P01-6845 Nevus Compound Oct.12, 2001 25 F Arm 22 C07-3665 A2 Melanoma Lentigo Maligna Jul. 31, 200634 M Head 23 CPC-03-00227 Nevus Spitz Jan. 1, 2003 74 F Torso 24CPC-07-03023 Nevus Compound Feb. 1, 2007 45 F arm 25 CPP-10-12821Melanoma Acral May 1, 2010 63 F Leg 26 CPP-10-03836 Nevus Dysplastic lowFeb. 1, 2010 37 M Torso 27 CPP-01-7511 Nevus Blue Nov. 9, 2001 25 F Arm28 CPC-06-430 Nevus Intradermal Jan. 11, 2006 40 F Head 29 CPP-10-1094Melanoma Nodular Jan. 4, 2010 54 M Arm 30 CPP-11-33384 Melanoma LentigoMaligna Nov. 3, 2011 67 F Head 31 CPP-12-23457 Melanoma DesmoplasticAug. 21, 2012 48 M Head 32 CPP-11-13421 Nevus Dysplastic low Apr. 29,2011 32 F Torso 33 CPP-11-15636 Nevus Compound May 19, 2011 22 M Torso34 CPP-10-9267 Nevus Dysplastic high/ Mar. 31, 2010 36 M TorsoJunctional 35 CPP-10-22878 Melanoma Superficial Aug. 6, 2010 67 F HeadSpreading 36 CPP-10-9477 A Nevus Dysplastic low/ Apr. 4, 2010 40 M TorsoJunctional 37 CPP-10-7786 Nevus Dysplastic low/ Mar. 18, 2010 52 M TorsoJunctional 38 CPP-10-18433 Nevus Compound Jun. 24, 2010 48 M Leg 39CPP-08-7203 Nevus Dysplastic low May 19, 2008 38 F Leg 40 CPP-10-19572Nevus Spitz Jul. 7, 2010 29 F Torso 41 CPP-11-13681 Nevus Dysplastic lowMay 3, 2011 48 M Torso 42 CPP-12-14855 Melanoma Acral May 25, 2012 71 MLeg 43 CPP-11-19854 Nevus Dysplastic low/ Jun. 27, 2011 31 M TorsoCompound 44 CPP-11-24358 Melanoma Lentigo Maligna Aug. 9, 2011 89 F Head45 CPP-10-9477 B Nevus Dysplastic low/ Apr. 1, 2010 40 M TorsoJunctional 46 CPP-10-16714 Melanoma Lentigo Maligna Jun. 10, 2010 58 MHead 47 CPP-12-17349 Melanoma Desmoplastic Jun. 20, 2012 62 M Leg 48CPP-10-8105 Nevus Dysplastic low/ Mar. 22, 2010 48 F Torso Junctional 49CPP-12-17027 Nevus Spitz Jun. 18, 2012 25 M Arm 50 CPP-11-17106 MelanomaSuperficial Jun. 2, 2011 52 M Head Spreading 51 CPP-12-14808 MelanomaDesmoplastic May 24, 2012 66 F Head 52 CPP-11-32490 Melanoma SuperficialOct. 27, 2011 58 F Arm Spreading 53 CPP-11-17723 Nevus Dysplastic lowJun. 8, 2011 36 F Torso

RNA extraction: The 31 group 1 samples were anonymized with a qPCRID anda 4 mm H&E slide was reviewed by a pathologist who circled the lesion ofinterest (either the nevus or the melanoma). The lesions were thenmacro-dissected and removed from three 10 μM slides. All three dissectedlesions from a single patient were pooled into a single tube. RNA wasextracted from samples, RNA expression levels were determined usingstandard qPCR techniques, and a standard CCP score was generated.

RNA from 30 of the samples was then used in the first two rounds ofbiomarker discovery. RNA was DNased and quantified. RNA withconcentrations >40 ng/uL were normalized to 40 ng/uL.

The 53 group 2 samples were anonymized with a qPCR ID, and a H&E slidewas reviewed by a pathologist who circled the lesion of interest. Thelesions were then macro-dissected from five 4 mm slides. All fivedissected lesions from a single patient were pooled into a single tube.The RNA from each tube was extracted using standard RNA extractiontechniques. RNA was DNased and quantified. All RNA samples withconcentrations >40 ng/uL were normalized to 40 ng/uL.

Measurement of gene expression: RNA expression was measured usingstandard qPCR techniques. Ct values were determined, and the expressionof each gene was normalized to the expression of housekeeper genes.

Biomarkers were assessed in three rounds of testing. Only samples fromgroup 1 were analyzed in rounds 1 and 2 of testing. Samples from groups1 and 2 were analyzed in round 3 of testing. Data from each round oftesting was analyzed separately and aggregated. The housekeeping genestested for normalization in all three rounds included MRFAP1, PSMA1,RPL13A, and TXNL1. In addition, housekeeping genes SLC25A3, RPS29, RPL8,PSMC1 and RPL4 were included in the first round of testing.

Results

Table 9 lists all amplicons used in biomarker discovery and listsp-values for separate rounds of analysis, and aggregated analysis asindicated in the round tested column. P-values are Wilcoxon Rank SumP-values, and p-values and AUC indicate differentiation all melanomassubtypes from all nevi subtypes.

TABLE 9 All amplicons used during biomarker discovery Round Gene AssayID P-Value AUC Tested ARPC2 Hs00194852_m1 4.1E−02 0.76 2 Hs01031743_m11.5E−01 0.60 1, 3 Hs01031746_g1 8.3E−03 0.81 1 Hs01031748_m1 8.8E−020.62 2, 3 BCL2A1 Hs00187845_m1 8.7E−06 0.80 1, 3 CCL3 (MIP-Hs00234142_m1 5.3E−08 0.87 2, 3 1α) Hs04194942_s1 2.5E−04 0.75 1, 3 FN1Hs01565276_m1 1.3E−06 0.82 1, 3 IFI6 Hs01564161_g1 3.8E−06 0.81 1, 3NCOA3 Hs01105241_m1 4.3E−01 0.55 1, 3 Hs01105267_m1 5.2E−01 0.54 1, 3PHIP Hs06611782_m1 1.8E−01 0.66 1 Hs01059904_m1 1.5E−01 0.60 1, 3 POU5F1Hs04195369_s1 5.0E−01 0.58 1, 3 Hs04260367_gH 1.1E−01 0.61 1, 3 RGS1Hs00175260_m1 5.2E−04 0.94 2 Hs01023770_g1 4.0E−11 0.94 1, 3Hs01023772_m1 1.0E−11 0.95 1, 3 SPP1 Hs00167093_m1 8.3E−09 0.87 2, 3Hs00959006_g1 3.3E−02 0.81 2 Hs00959008_g1 3.3E−09 0.88 2, 3Hs00959010_m1 5.3E−09 0.89 1 Hs00960641_m1 4.0E−01 1.00 1, 3 WNT2Hs00608222_m1 3.2E−01 0.92 1 Hs00608224_m1 4.5E−01 0.61 3 Hs01128652_m18.3E−01 0.55 1 WIF1 Hs00183662_m1 5.3E−02 0.63 1, 3 Hs01548029_m17.6E−01 0.54 1 NR4A1 Hs00374226_m1 2.9E−01 0.57 1, 3 Hs00926542_g11.0E−01 0.72 1 SOCS3 Hs00269575_s1 9.3E−09 0.87 1, 3 Hs01000485_g19.4E−08 0.85 2, 3 Hs02330328_s1 5.4E−03 0.84 1 PRAME Hs00196132_m12.0E−02 0.88 2 Hs01022299_m1 1.0E+0 0.50 2 Hs01022301_m1 1.3E−09 0.89 2,3 Hs04186846_m1 3.8E−06 0.84 2, 3 KRT15 Hs00267035_m1 1.0E−01 0.71 2Hs00951967_g1 1.3E−08 0.89 2, 3 Hs00951968_gH 1.0E−08 0.89 2, 3Hs02558897_s1 8.3E−02 0.74 2 FABP7 Hs00361424_g1 1.2E−05 0.80 2, 3Hs00361426_m1 3.5E−02 0.77 2 Hs00953719_g1 1.7E−03 0.72 2, 3 CFHHs00962362_g1 1.4E−03 0.72 2, 3 Hs00962365_g1 5.0E−01 0.59 2Hs00962373_m1 6.3E−03 0.69 2, 3 PTN Hs00383235_m1 1.5E−02 0.67 2, 3Hs01085690_m1 5.5E−02 0.75 2 Hs01085691_m1 1.4E−03 0.72 2, 3 HEY1Hs01114111_g1 1.0E−04 0.76 2, 3 Hs01114112_g1 4.5E−04 0.74 2, 3Hs01114113_m1 6.9E−01 0.56 2 GDF15 Hs00171132_m1 5.0E−02 0.64 2, 3PHACTR1 Hs01116208_m1 9.4E−02 0.72 2 Hs01116210_m1 2.1E−01 0.59 2, 3Hs01116212_m1 4.8E−02 0.76 2 Hs01116214_m1 2.1E−07 0.86 2, 3 LCP2Hs00175501_m1 2.2E−07 0.89 3 Hs01092638_m1 2.4E−06 0.86 3 CXCL9Hs00171065_m1 4.7E−11 0.97 3 Hs00970537_m1 3.6E−11 0.97 3 CXCL10Hs01124251_g1 8.9E−07 0.92 3 Hs01124252_g1 8.1E−12 0.98 3 CXCL13Hs00757930_m1 4.0E−10 0.96 3 Hs99999094_m1 4.5E−09 0.95 3 S100A9Hs00610058_m1 6.1E−05 0.82 3 SERPINB4 Hs00741313_g1 1.6E−01 0.65 3 CCL19Hs00171149_m1 8.3E−02 0.64 3 CCL5 Hs00174575_m1 1.5E−10 0.96 3 CD38Hs01120071_m1 2.2E−09 0.95 3 CXCL12 Hs00171022_m1 3.5E−01 0.58 3 HCLS1Hs00945386_m1 1.1E−03 0.76 3 HLA-DMA Hs00185435_m1 3.7E−04 0.78 3HLA-DPA1 Hs01072899_m1 3.8E−02 0.67 3 HLA-DPB1 Hs00157955_m1 2.9E−010.59 3 HLA-DRA Hs00219575_m1 6.4E−04 0.77 3 HLA-E Hs03045171_m1 1.1E−010.63 3 IGHM Hs00378435_m1 1.8E−02 0.76 3 IGJ Ha00950678_g1 5.2E−03 0.733 IGLL5; CKAP2 Hs00382306_m1 1.8E−01 0.64 3 IRF1 Hs00971966_g1 2.0E−080.92 3 IRF4 Hs00180031_m1 2.4E−02 0.69 3 ITGB2 Hs01051739_m1 1.7E−050.84 3 PECAM1 Hs00169777_m1 1.8E−03 0.75 3 PTPN22 Hs00249262_m1 1.4E−050.84 3 PTPRC Hs00894732_m1 1.5E−04 0.80 3 SELL Hs01046459_m1 9.0E−090.93 3

FIG. 3 shows the distribution of all amplicons tested in all threerounds of biomarker discovery. Samples are differentiated based ondiagnosis on the Y-axis. The relative expression (Ct) of each gene(compared to the expression of the housekeeper genes) is graphed on theX axis

FIG. 4 shows the distributions of each individual amplicon tested inrounds one and two of biomarker discovery. Samples are differentiatedbased on their pathological subtype on the Y axis. The relativeexpression (Ct) of each gene (compared to the expression of thehousekeeper genes) is graphed on the X axis. Each amplicon is identifiedby the gene and the last three digits of the assay ID

Discussion

These data strongly indicate that expression of specific biomarker genescan be used to differentiate malignant melanoma from non-malignant nevi.Nonetheless, a larger dataset is needed to determine with higherconfidence how effective each individual biomarker is in differentiatingmelanoma, as well as to determine how heavily each biomarker would needto be weighted in a diagnostic model.

Table 10 lists the best performing assay/amplicon of each biomarkersdetermined by its AUC, P-value, and lack of missing data. P-values areWilcoxon Rank Sum P-values. P-values and AUC indicate differentiation ofall melanomas subtypes from all nevi subtypes.

TABLE 10 Gene Assay ID P-Value AUC CXCL10 Hs01124252_g1 8.1E−12 0.98CXCL9 Hs0171065_m1 4.7E−11 0.97 CXCL13 Hs00757930_m1 4.0E−10 0.96 CCL5Hs00174575_m1 1.5E−10 0.96 RGS1 Hs01023772_m1 1.0E−11 0.95 CD38Hs01120071_m1 2.2E−09 0.95 SELL Hs01046459_m1 9.0E−09 0.93 IRF1Hs00971966_g1 2.0E−08 0.92 SPP1 Hs00959010_m1 5.3E−09 0.89 PRAMEHs01022301_m1 1.3E−09 0.89 KRT15 Hs00951967_g1 1.3E−08 0.89 LCP2Hs00175501_m1 2.2E−07 0.89 CCL3 (MIP-1α) Hs00234142_m1 5.3E−08 0.87PHACTR1 Hs01116217_m1 2.1E−07 0.86 ITGB2 Hs01051739_m1 1.7E−05 0.84PTPN22 Hs00249262_m1 1.4E−05 0.84 FN1 Hs01565276_m1 1.3E−06 0.82 S100A9Hs00610058_m1 6.1E−05 0.82 IFI6 Hs01564161_m1 3.8E−06 0.81 BCL2A1Hs00187845_m1 8.7E−06 0.80 FABP7 Hs00361424_g1 1.2E−05 0.80 PTPRCHs00894732_m1 1.5E−04 0.80 HLA-DMA Hs00185435_m1 3.7E−04 0.78 HLA-DRAHs00219575_m1 6.4E−04 0.77 HCLS1 Hs00945386_m1 1.1E−03 0.76 PECAM1Hs00169777_m1 1.8E−03 0.75 HEY1 Hs01114112_g1 4.5E−04 0.74 IGJHa00950678_g1 5.2E−03 0.73 CFH Hs00962362_g1 1.4E−03 0.72 PTNHs01085691_m1 1.4E−03 0.72

Example 4

P-values were calculated for distinguishing melanoma from nevi for allcombinations of two, three, and four genes from Panel I with data fromthe same samples used above. Firth's logistic regression was used toassign the best weights to each of the genes in each combination. Thep-values were calculated using a likelihood ratio test comparing a modelcontaining all genes in each combination with a reduced model containingno predictor variables. The number of samples with data for all genes ineach combination and whether the combination contains CCP genes, othergenes, or a mix of CCP and other genes are included in the results.

Additionally, p-values were calculated for average CCP expression of allcombinations of one to ten of the CCP genes in panel C as continuing tothe next stage for use in a potential training set. The average of eachcombination of CCP genes was calculated and a t-test was performed totest for a difference in average expression between melanomas and nevi.

Table WW (3330-01-1P-2013-03-15-TABLEWW-MSG.txt) contains the resultsfor the combinations of CCP gene averages. Table XX(3330-01-1P-2013-03-15-TABLEXX-MSG.txt) contains the results for allcombinations of two genes. Table YY(3330-01-1P-2013-03-15-TABLEYY-MSG.txt) contains the results for allcombinations of three genes. Table ZZ(3330-01-1P-2013-03-15-TABLEZZ-MSG.txt) contains the results for allcombinations of four genes.

Example 5

In this example we determined a model for differentiating melanomasamples from nevus samples based on a computed score.

Methods

Approximately 600 skin lesions were acquired from two separate sites(coded “Munich” and “Provitro”), with relatively equal numbers from bothlocations. Each site provided both malignant and benign samples, withall major histological subtypes represented. The diagnosis for each casewas confirmed, using a second dermatopathologist who was blinded to thediagnosis of the first dermatopathologist. If there was discordance, athird dermatopathologist adjudicated the diagnosis.

An H&E stained slide from each case was then reviewed by a pathologist,and the lesion of interest identified for each case. The correspondingtissue was macrodissected from 5 unstained slides (4 mm thickness) andpooled into a single tube. The RNA was then extracted from the tissue,the RNA was DNAsed using DNAse I and cDNA synthesized. We thenpre-amplified all genes of interest including 7 housekeepernormalization genes) in one multiplex reaction. Finally, quantitativePCR was used to measure the expression of each gene. The expressionvalues were calculated by determining the CT (Crossing Threshold) ofeach gene. Each sample was run in triplicate by splitting each sampleinto 3 aliquots after the cDNA synthesis.

The three measurements of each gene were then averaged and normalized bythe averaged expression of all seven housekeeper genes. Each gene wasstudied to determine if its expression could differentiate betweenmalignant melanoma and benign nevi samples; if genes were effective atthis, they were further analyzed to see which genes had correlating data(an indication that these genes measure the same biological pathway).Genes with correlating data were grouped together in sets, with theaverage expression of the set used to differentiate melanoma and nevi.

Results

We acquired ˜600 samples from two German sites (labeled Munich andProvitro [Berlin]). Each site contributed roughly even numbers ofmalignant and benign samples, with all major histologic subtypesrepresented in the samples from each site. We first identified thelesion of interest in each sample and then extracted the RNA from eachsample and measured the RNA expression level of the potential signaturegenes and 7 housekeeper normalization genes (Table 11).

TABLE 11 List of potential signature genes tested and housekeeper genesused for normalization. Gene Function Gene Function PTN PotentialSignature CD38 Potential Signature CFH Potential Signature RGS1Potential Signature IGJ Potential Signature DLGAP5 Potential SignatureHEY1 Potential Signature MCM10 Potential Signature PECAM1 PotentialSignature RRM2 Potential Signature HCLS1 Potential Signature CCL5Potential Signature HLA-DRA Potential Signature CXCL13 PotentialSignature HLA-DMA Potential Signature CXCL9 Potential Signature BCL2A1Potential Signature CENPF Potential Signature FABP7 Potential SignaturePBK Potential Signature PTPRC Potential Signature CXCL10 PotentialSignature IFI6 Potential Signature FOXM1 Potential Signature S100A9Potential Signature PLK1 Potential Signature FN1 Potential SignatureCEP55 Potential Signature ITGB2 Potential Signature DTL PotentialSignature PTPN22 Potential Signature SKA1 Potential Signature PHACTR1Potential Signature CCL5 Potential Signature CCL3 Potential SignatureCLTC Housekeeper PRAME Potential Signature MRFAP1 Housekeeper KRT15Potential Signature PPP2CA Housekeeper SPP1 Potential Signature PSMA1Housekeeper LCP2 Potential Signature RPL13A Housekeeper IRF1 PotentialSignature RPL8 Housekeeper SELL Potential Signature TXNL1 Housekeeper

The ability of each gene was then analyzed to determine if itsexpression was effective in differentiating the malignant melanoma andbenign nevi samples. We determined that PRAME was a very effectivebiomarker (FIG. 5 ). Furthermore, we also found that a large number ofimmune genes were also able to strongly differentiate melanoma and nevi.We further investigated 8 of these immune genes and found that theirdata was highly correlated, as the expression of each individual immunegene had a linear relationship with the average of all 8 immune genes(FIG. 6 ). This may indicate that they are measuring the same biologicalprocess, and they were thus grouped together into an “immune” set. Theaveraged expression of all 8 immune genes was calculated and then usedwhen analyzing the dataset. Finally, we also noted that the cell cyclegene S100A9 also was able to differentiate many melanoma and nevisamples.

We next created a combined diagnostic model, using these three sets ofbiomarkers (PRAME, the immune genes, and S100A9). When the expression ofeach biomarker was graphed against the other, we did not see a highcorrelation, indicating that each biomarker is likely measuring adifferent biological process and has independent value (see FIG. 7 ). Weran the data through a logistic regression model, to best determine howeach biomarker could be weighted in the model, to maximize the model'sability to differentiate melanoma and nevi. The most effective model wasfound with the following weightings for the expression of each biomarkerset: (0.525×PRAME)+(0.677×Immune)+(0.357×S100A9). The model then takesthe data from each patient and generates a score, which can be used todifferentiate melanoma and nevi (see FIG. 8 ). This current dataset wasthen used to generate a ROC curve (see FIG. 9 ), which had an AUC of˜0.96.

Example 6

In this example we determined a diagnostic model for differentiatingbetween malignant melanoma and non-malignant nevi and then furtherrefined the diagnostic model.

Methods

Patient samples were acquired and prepared as described above in Example5. Likewise, RNA extraction, preparation, and quantification of geneexpression were also carried out in the same fashion as described abovein Example 5. The same list of potential signature genes and the samehousekeeper genes were assayed as in Example 5 (Table 11). Like Example5, the measured expression of each gene was averaged and normalized bythe averaged expression of all seven housekeeper genes. Each gene wasanalyzed to determine if its expression could differentiate betweenmalignant melanoma samples and benign nevi samples. Genes that wereeffective at differentiating between malignant melanoma samples andbenign nevi samples were further analyzed to determine which genes hadcorrelating data. Correlating data indicated that the genes measured thesame biological pathway. Genes with correlating data were groupedtogether in sets, with the average expression of the set used todifferentiate melanoma and nevi.

Results

Building the Diagnostic Model

As described in Example 5, each gene was analyzed to determine if itsexpression was effective in differentiating the malignant melanomasamples from the benign nevi samples. In this analysis, we used forwardselection to choose predictors for inclusion in a diagnostic model thatwould differentiate melanoma and nevi samples. As in Example 5, weselected PRAME, S100A9, and an immune component score as the predictorsto differentiate the malignant melanoma samples from the benign nevisamples. The immune component score was made up of eight immune genes(CXCL9, CCL5, CXCL10, IRF1, PTPN22, PTPRC, LCP2, AND CD38) that all hadhighly correlated data (FIG. 6 ). These eight immune genes were groupedand their values averaged to create an immune component score. As inExample 5, we also determined that these three predictors (PRAME,S100A9, or the immune component) had expression patterns only moderatelycorrelated with each other (FIG. 7 ), indicating that each predictorlargely provides independent information when distinguishing melanomaand nevi samples.

We next created a combined diagnostic model using PRAME, S100A9, and theimmune component score. The combined diagnostic model that we createdwas a linear model based on the gene expression data from the threepredictors (PRAME, S100A9 and the immune component). In generating thelinear model, we used generalized logistic regression to calculate thebest weightings for each of the three predictors that would mosteffectively differentiate the melanoma and nevi samples in this dataset.The calculated best weightings were as follows: 1.149 for PRAME, 0.922for S100A9, and 0.698 for the immune component score. Using these bestweightings, the gene expression data for the three predictors was thenused to generate a score that could be used to help differentiatebetween benign nevi samples and melanoma samples. The gene expressiondata for each predictor was multiplied by the respective best weightingand then the three weighted values were combined. This combined value,as shown here, represented a score that could be used to helpdifferentiate between benign nevi samples and melanoma samples:

Score = (1.149 × PRAME) + (0.922 × S 100A 9) + (0.698 × Immune  component)

We then generated and plotted scores for all 544 samples (FIG. 10 ). Inthe plotted scores we observed a bi-modal distribution. Nearly allsamples with a score greater than zero were melanoma (dark gray bars),while nearly all nevi samples (light gray bars) had a score less thanzero. Using this data, a ROC curve was generated that had an AUC of˜0.95 and an associated p-value of 2.0×10−63 (FIG. 11 ). From this ROCcurve we selected a cutoff point to differentiate between melanomasamples and nevi samples. In selecting the cutoff point we sought tomaximize the sensitivity of the model, while maintaining the highestpossible specificity. We selected a cutoff point with a sensitivity of0.89 and a specificity of 0.93. We then adjusted the calculation of thescore so that the selected cutoff point would be at a value of zero.Thus the adjusted score calculation was:

$\begin{matrix}{Adjusted} \\{score}\end{matrix} = {\begin{matrix}\left( {1.149 \times} \right. \\\left. {PRAME} \right)\end{matrix} + \begin{matrix}\left( {0.922 \times} \right. \\\left. {S\; 100A\; 9} \right)\end{matrix} + \begin{matrix}\left( {0.698 \times {Immune}} \right. \\\left. {component} \right)\end{matrix} - 0.334}$

Refining the Diagnostic Model

To improve the robustness and precision of the assay, we wanted todetermine additional measurements for the PRAME and S100A9 predictors.We tested additional amplicons corresponding to PRAME and S100A9. Wesought to determine if any other amplicons produced a reliable andcorrelated signal for these genes. We tested four additional PRAMEamplicons.

We also tested amplicons that corresponded to six other genes that wedetermined would have expression that was highly correlated to S100A9:S100A7, S100A8, S100A10, S100A12, S100A14 and PI3.

We determined that one PRAME amplicon had low failure rates and hadvalues highly correlated with the PRAME amplicon previously used in themodel. We averaged its measurement with the other PRAME measurement toprovide a PRAME component.

We chose four other potential genes which produced data with low failurerates and were highly correlated with the S100A9 amplicon selected inthe training set. These four genes, S100A7, S100A8, S100A12, and PI3were averaged with S100A9 to yield a single S100-related component, orS100 score.

Thus, we were able to create a refined signature for the threepredictors, PRAME, S100-related component, and the immune component. Therefined signature included additional measurements for the PRAME andS100-related predictors determined above. The refined signature includeda total of 15 amplicons that measured 14 signature genes comprising twomeasurements of the PRAME gene, one measurement each of S100 relatedgenes S100A9, S100A7, S100A8, S100A12, and PI3, and measurements ofeight highly correlated immune genes (CXCL9, CCL5, CXCL10, IRF1, PTPN22,PTPRC, LCP2, AND CD38)(Table 12). Along with these 15 signatureamplicons, we also included amplicons corresponding to nine differenthousekeeper genes for normalization, for a total of 24 amplicons in ourrefined signature (Table 12).

TABLE 12 Signature and housekeeper genes comprising the refinedsignature. Gene Amplicons Component PRAME 2 PRAME S100A7 1 S100-relatedS100A8 1 S100A9 1 S100A12 1 PI3 1 CCL5 1 Immune CD38 1 CXCL10 1 CXCL9 1IRF1 1 LCP2 1 PTPRC 1 SELL 1 CLTC 1 Housekeeper MRFAP1 1 PPP2CA 1 PSMA11 RPL13A 1 RPL8 1 RPS29 1 SLC25A3 1 TXNL1 1

Lastly, we performed a concordance study to verify that the changes tothe multiplex PCR reaction would not alter the data generated from the10 signature amplicons retained from the initial signature. The qPCRassay relies on the fact that all the measured genes are pre-amplifiedin a single multiplex PCR reaction. Since the refined signature differedfrom the initial signature, it was important to ascertain that thedifferent amplicon set of the refined signature did not alter themultiplex PCR reaction and consequently alter the data generated fromthe 10 amplicons retained from the initial signature set. Therefore, weretested RNA expression in 74 RNA samples from the initial training setusing the refined signature. The scores of the retested RNA samplesdemonstrated an extremely high correlation (correlation coefficient of0.99) when compared to the scores generated from the training set. Thus,the refined signature did not alter the data generated from any of theamplicons of the refined signature.

Accordingly, the refined signature produced a refined, adjusted scorecalculation of:

$\begin{matrix}{{Refined},} \\{adjusted} \\{score}\end{matrix} = {\begin{matrix}\left( {1.149 \times} \right. \\{PRAME} \\\left. {component} \right)\end{matrix} + \begin{matrix}\left( {0.922 \times S\; 100} \right. \\\left. {component} \right)\end{matrix} + \begin{matrix}\left( {0.698 \times} \right. \\{Immune} \\\left. {component} \right)\end{matrix} - 0.334}$

Example 7

In this example we clinically validated a diagnostic model fordifferentiating between malignant melanoma and non-malignant nevi.

Methods

A validation cohort was generated by acquiring more than 400 skinlesions from four separate sites: Cleveland Clinic, Moffit CancerCenter, Northwestern University and the University of Utah (Table 13).

TABLE 13 Sources of samples for the clinical validation cohort.Diagnosis Institution Benign Malignant Total Cleveland Clinic 62 65 127Moffit Cancer Center 80 57 137 Northwestern University 26 46 72University of Utah 58 43 101 Total 226 211 437 Only samples thatproduced analyzable results were included.

Each site provided both malignant and benign samples, with all majorhistological subtypes represented. The diagnosis for each case wasconfirmed, using a second dermatopathologist who was blinded to thediagnosis of the first dermatopathologist. If there was discordance inthe diagnoses, a third dermatopathologist adjudicated the diagnosis. Themelanoma subtypes included superficial spreading melanoma, nodularmelanoma, lentigo maligna melanoma, desmoplastic melanoma, and subtypesclassified as a not otherwise specified (Table 14).

TABLE 14 Clinical information for melanoma samples used in the clinicalvalidation. SSM Nod Acral LMM Desm NOS Overall Total Number 105 38 9 315 23 211 SSM = Superficial Spreading Melanoma, Nod = Nodular melanoma,LMM = Lentigo Maligna Melanoma, Desm = Desmoplastic, NOS = Not OtherwiseSpecified subtype Only samples that produced analyzable results wereincluded.

The nevi subtypes included blue nevi, compound nevi, junctional nevi,dermal nevi, deep penetrating nevi, dysplastic nevi, and subtypesclassified as a not otherwise specified (Table 15).

TABLE 15 Clinical information for nevi samples used in the clinicalvalidation. Blue Comp Junc Spitz Derm Deep Pen NOS Overall Dyspl* TotalNumber 22 101 20 7 41 7 28 226 70 Comp = Compound, June = Junctional,Derm = Dermal, Deep Pen = Deep Penetrating, NOS = Not OtherwiseSpecified subtype, Dyspl = Dysplastic. *Dysplastic nevi were scored asan attribute of a subtype, not as a specific subtype.

The patient samples were prepared as described above in Example 5. RNAextraction, RNA preparation, and quantification of gene expression werealso carried out as described above in Example 5. The same list ofpotential signature genes and the same housekeeper genes were assayed asin Example 5 (Table 11). Likewise, as in Example 5, the measuredexpression of each gene was averaged and normalized by the averagedexpression of all seven housekeeper genes. Then, a refined, adjustedscore was generated for each patient sample using the refined, adjustedscore calculation of:

$\begin{matrix}{{Refined},} \\{adjusted} \\{score}\end{matrix} = {\begin{matrix}\left( {1.149 \times} \right. \\{PRAME} \\\left. {component} \right)\end{matrix} + \begin{matrix}\left( {0.922 \times S\; 100} \right. \\\left. {component} \right)\end{matrix} + \begin{matrix}\left( {0.698 \times} \right. \\{Immune} \\\left. {component} \right)\end{matrix} - 0.334}$Results

The refined, adjusted scores were plotted for each patient sample (FIG.12 ). The plotted refined, adjusted scores resulted in a bimodaldistribution similar to that seen in FIG. 10 of Example 6. Nearly allmelanoma samples had scores greater than zero (FIG. 12 , upper panel)and nearly all nevi samples had scores less than zero (FIG. 12 , lowerpanel). This data was then used to generate a validation ROC curve thathad an AUC of ˜0.96 with a sensitivity of 0.9 and a specificity of 0.91(FIG. 13 ). The p-value associated with the validation ROC curve was3.7×10⁻⁶³. The validation ROC curve was then compared to the ROC curvegenerated in Example 6 (FIG. 11 ) to determine if the diagnostic modelhad been validated. The validation ROC curve had an AUC of ˜0.96, asensitivity of 0.9, and a specificity of 0.91 compared to the ROC curveof Example 6 which had an AUC of ˜0.95, a sensitivity of 0.89, and aspecificity of 0.93. The close agreement of the validation ROC curve andthe ROC curve of Example 6 indicated that the validation cohortvalidated the diagnostic model of Example 6.

Next, the performance of the diagnostic model was analyzed withinindividual histological subtypes for those subtypes with 30 or moresamples (Table 16).

TABLE 16 Assay performance within individual subtypes in the clinicalvalidation. Correct Incorrect Sen- Subtype call call sitivitySpecificity Compound Nevus 95 6 94% Dermal Nevus 40 1 98% All Nevi 20620 91% Superficial Spread Melanoma 90 15 86% Nodular Melanoma 37 1 97%Lentigo Maligna Melanoma 28 3 90% All Melanomas 189 22 90% Only subtypesgroups with ≥30 samples were reported.

There were two benign subtypes, compound and dermal, with more than 30samples. The scoring of compound and dermal subtypes resulted inspecificities of 94% and 98%, respectively, with an overall specificityof 91% for all nevi. There were three malignant subtypes, superficialspreading melanoma, nodular melanoma, and lentigo maligna melanoma withmore than 30 samples. The scoring of superficial spreading melanoma,nodular melanoma, and lentigo maligna melanoma resulted in sensitivitiesof 86%, 97%, and 90%, respectively, with an overall sensitivity of 90%for all melanomas.

All publications and patent applications mentioned in the specificationare indicative of the level of those skilled in the art to which thisinvention pertains. All publications and patent applications are hereinincorporated by reference to the same extent as if each individualpublication or patent application was specifically and individuallyindicated to be incorporated by reference. The mere mentioning of thepublications and patent applications does not necessarily constitute anadmission that they are prior art to the instant application.

Although the foregoing invention has been described in some detail byway of illustration and example for purposes of clarity ofunderstanding, it will be obvious that certain changes and modificationsmay be practiced within the scope of the appended claims.

What is claimed is:
 1. A method for treating melanoma in a patient, themethod comprising: measuring in a sample from the patient mRNAexpression of a panel of genes normalized to one or more housekeepinggenes, the panel of genes comprising a combination of: PRAME;S100-related genes S100A9, S100A7, S100A8, S100A12, and P13; and one ormore immune genes selected from CXCL9, CCL5, CXCL10, IRF1, PTPN22,PTPRC, LCP2, SELL, and CD38; calculating a combined score of themeasured mRNA expression of the panel of genes; classifying the sampleas being a malignant melanoma based on the combined score exceeding areference value; and administering to the patient having the malignantmelanoma a treatment comprising one or more of: excising the malignantmelanoma; excising the malignant melanoma and a border of normal skin,reexcising the site of the sample to remove additional tissue;reexcising additional tissue surrounding a biopsy site; removal of alymph node; lymph node dissection; chemotherapy; radiation therapy;biological therapy; immunotherapy; and targeted therapy.
 2. The methodof claim 1, wherein the panel of genes further comprises S100A10 andS100A14.
 3. The method of claim 1, wherein the one or more immune genescomprises CXCL9.
 4. The method of claim 1, wherein the housekeepinggenes comprise one or more of CLTC, MRFAP1, PSMA1, RPL13A, TXNL1,SLC25A3, RPS29, RPL8, PSMC1, and RPL4.
 5. The method of claim 1, furthercomprising combining the combined score with a value of a clinicalparameter.
 6. The method of claim 5, wherein the clinical parameter is aclinical staging.
 7. The method of claim 1, wherein the sample is a skinlesion.
 8. The method of claim 1, wherein the sample is an FFPE sample,a fresh frozen sample, or contains predominantly melanoma suspect cells.9. The method of claim 1, wherein the sample is a malignant melanoma.10. The method of claim 1, wherein the sample is a superficial spreadingmelanoma, a nodular melanoma, a lentigo maligna melanoma, a desmoplasticmelanoma, or an acral melanoma.
 11. The method of claim 1, wherein theexpression of the panel of genes is measured by qPCR.
 12. The method ofclaim 1, wherein the expression of the panel of genes is measured byhybridization.
 13. The method of claim 1, wherein the combined score isrecorded in a report.
 14. The method of claim 1, wherein the combinedscore is recorded in a report communicated to the patient's medicalprovider.
 15. The method of claim 1, wherein the combined score isrecorded in a report communicated to the patient's medical provider whoadministers treatment to the patient.
 16. The method of claim 1, whereinthe combined score is recorded in a report with an additional clinicalparameter value communicated to the patient's medical provider whoadministers treatment to the patient.
 17. The method of claim 1, whereinthe treatment further comprises watchful waiting.